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• Published: 12 February 2024

Education reform and change driven by digital technology: a bibliometric study from a global perspective

• Chengliang Wang 1 ,
• Xiaojiao Chen 1 ,
• Teng Yu   ORCID: orcid.org/0000-0001-5198-7261 2 , 3 ,
• Yidan Liu 1 , 4 &
• Yuhui Jing 1  

Humanities and Social Sciences Communications volume  11 , Article number:  256 ( 2024 ) Cite this article

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• Development studies
• Science, technology and society

Amidst the global digital transformation of educational institutions, digital technology has emerged as a significant area of interest among scholars. Such technologies have played an instrumental role in enhancing learner performance and improving the effectiveness of teaching and learning. These digital technologies also ensure the sustainability and stability of education during the epidemic. Despite this, a dearth of systematic reviews exists regarding the current state of digital technology application in education. To address this gap, this study utilized the Web of Science Core Collection as a data source (specifically selecting the high-quality SSCI and SCIE) and implemented a topic search by setting keywords, yielding 1849 initial publications. Furthermore, following the PRISMA guidelines, we refined the selection to 588 high-quality articles. Using software tools such as CiteSpace, VOSviewer, and Charticulator, we reviewed these 588 publications to identify core authors (such as Selwyn, Henderson, Edwards), highly productive countries/regions (England, Australia, USA), key institutions (Monash University, Australian Catholic University), and crucial journals in the field ( Education and Information Technologies , Computers & Education , British Journal of Educational Technology ). Evolutionary analysis reveals four developmental periods in the research field of digital technology education application: the embryonic period, the preliminary development period, the key exploration, and the acceleration period of change. The study highlights the dual influence of technological factors and historical context on the research topic. Technology is a key factor in enabling education to transform and upgrade, and the context of the times is an important driving force in promoting the adoption of new technologies in the education system and the transformation and upgrading of education. Additionally, the study identifies three frontier hotspots in the field: physical education, digital transformation, and professional development under the promotion of digital technology. This study presents a clear framework for digital technology application in education, which can serve as a valuable reference for researchers and educational practitioners concerned with digital technology education application in theory and practice.

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Introduction

Digital technology has become an essential component of modern education, facilitating the extension of temporal and spatial boundaries and enriching the pedagogical contexts (Selwyn and Facer, 2014 ). The advent of mobile communication technology has enabled learning through social media platforms (Szeto et al. 2015 ; Pires et al. 2022 ), while the advancement of augmented reality technology has disrupted traditional conceptions of learning environments and spaces (Perez-Sanagustin et al., 2014 ; Kyza and Georgiou, 2018 ). A wide range of digital technologies has enabled learning to become a norm in various settings, including the workplace (Sjöberg and Holmgren, 2021 ), home (Nazare et al. 2022 ), and online communities (Tang and Lam, 2014 ). Education is no longer limited to fixed locations and schedules, but has permeated all aspects of life, allowing learning to continue at any time and any place (Camilleri and Camilleri, 2016 ; Selwyn and Facer, 2014 ).

The advent of digital technology has led to the creation of several informal learning environments (Greenhow and Lewin, 2015 ) that exhibit divergent form, function, features, and patterns in comparison to conventional learning environments (Nygren et al. 2019 ). Consequently, the associated teaching and learning processes, as well as the strategies for the creation, dissemination, and acquisition of learning resources, have undergone a complete overhaul. The ensuing transformations have posed a myriad of novel issues, such as the optimal structuring of teaching methods by instructors and the adoption of appropriate learning strategies by students in the new digital technology environment. Consequently, an examination of the principles that underpin effective teaching and learning in this environment is a topic of significant interest to numerous scholars engaged in digital technology education research.

Over the course of the last two decades, digital technology has made significant strides in the field of education, notably in extending education time and space and creating novel educational contexts with sustainability. Despite research attempts to consolidate the application of digital technology in education, previous studies have only focused on specific aspects of digital technology, such as Pinto and Leite’s ( 2020 ) investigation into digital technology in higher education and Mustapha et al.’s ( 2021 ) examination of the role and value of digital technology in education during the pandemic. While these studies have provided valuable insights into the practical applications of digital technology in particular educational domains, they have not comprehensively explored the macro-mechanisms and internal logic of digital technology implementation in education. Additionally, these studies were conducted over a relatively brief period, making it challenging to gain a comprehensive understanding of the macro-dynamics and evolutionary process of digital technology in education. Some studies have provided an overview of digital education from an educational perspective but lack a precise understanding of technological advancement and change (Yang et al. 2022 ). Therefore, this study seeks to employ a systematic scientific approach to collate relevant research from 2000 to 2022, comprehend the internal logic and development trends of digital technology in education, and grasp the outstanding contribution of digital technology in promoting the sustainability of education in time and space. In summary, this study aims to address the following questions:

RQ1: Since the turn of the century, what is the productivity distribution of the field of digital technology education application research in terms of authorship, country/region, institutional and journal level?

RQ2: What is the development trend of research on the application of digital technology in education in the past two decades?

RQ3: What are the current frontiers of research on the application of digital technology in education?

Literature review

Although the term “digital technology” has become ubiquitous, a unified definition has yet to be agreed upon by scholars. Because the meaning of the word digital technology is closely related to the specific context. Within the educational research domain, Selwyn’s ( 2016 ) definition is widely favored by scholars (Pinto and Leite, 2020 ). Selwyn ( 2016 ) provides a comprehensive view of various concrete digital technologies and their applications in education through ten specific cases, such as immediate feedback in classes, orchestrating teaching, and community learning. Through these specific application scenarios, Selwyn ( 2016 ) argues that digital technology encompasses technologies associated with digital devices, including but not limited to tablets, smartphones, computers, and social media platforms (such as Facebook and YouTube). Furthermore, Further, the behavior of accessing the internet at any location through portable devices can be taken as an extension of the behavior of applying digital technology.

The evolving nature of digital technology has significant implications in the field of education. In the 1890s, the focus of digital technology in education was on comprehending the nuances of digital space, digital culture, and educational methodologies, with its connotations aligned more towards the idea of e-learning. The advent and subsequent widespread usage of mobile devices since the dawn of the new millennium have been instrumental in the rapid expansion of the concept of digital technology. Notably, mobile learning devices such as smartphones and tablets, along with social media platforms, have become integral components of digital technology (Conole and Alevizou, 2010 ; Batista et al. 2016 ). In recent times, the burgeoning application of AI technology in the education sector has played a vital role in enriching the digital technology lexicon (Banerjee et al. 2021 ). ChatGPT, for instance, is identified as a novel educational technology that has immense potential to revolutionize future education (Rospigliosi, 2023 ; Arif, Munaf and Ul-Haque, 2023 ).

Pinto and Leite ( 2020 ) conducted a comprehensive macroscopic survey of the use of digital technologies in the education sector and identified three distinct categories, namely technologies for assessment and feedback, mobile technologies, and Information Communication Technologies (ICT). This classification criterion is both macroscopic and highly condensed. In light of the established concept definitions of digital technology in the educational research literature, this study has adopted the characterizations of digital technology proposed by Selwyn ( 2016 ) and Pinto and Leite ( 2020 ) as crucial criteria for analysis and research inclusion. Specifically, this criterion encompasses several distinct types of digital technologies, including Information and Communication Technologies (ICT), Mobile tools, eXtended Reality (XR) Technologies, Assessment and Feedback systems, Learning Management Systems (LMS), Publish and Share tools, Collaborative systems, Social media, Interpersonal Communication tools, and Content Aggregation tools.

Methodology and materials

Research method: bibliometric.

The research on econometric properties has been present in various aspects of human production and life, yet systematic scientific theoretical guidance has been lacking, resulting in disorganization. In 1969, British scholar Pritchard ( 1969 ) proposed “bibliometrics,” which subsequently emerged as an independent discipline in scientific quantification research. Initially, Pritchard defined bibliometrics as “the application of mathematical and statistical methods to books and other media of communication,” however, the definition was not entirely rigorous. To remedy this, Hawkins ( 2001 ) expanded Pritchard’s definition to “the quantitative analysis of the bibliographic features of a body of literature.” De Bellis further clarified the objectives of bibliometrics, stating that it aims to analyze and identify patterns in literature, such as the most productive authors, institutions, countries, and journals in scientific disciplines, trends in literary production over time, and collaboration networks (De Bellis, 2009 ). According to Garfield ( 2006 ), bibliometric research enables the examination of the history and structure of a field, the flow of information within the field, the impact of journals, and the citation status of publications over a longer time scale. All of these definitions illustrate the unique role of bibliometrics as a research method for evaluating specific research fields.

This study uses CiteSpace, VOSviewer, and Charticulator to analyze data and create visualizations. Each of these three tools has its own strengths and can complement each other. CiteSpace and VOSviewer use set theory and probability theory to provide various visualization views in fields such as keywords, co-occurrence, and co-authors. They are easy to use and produce visually appealing graphics (Chen, 2006 ; van Eck and Waltman, 2009 ) and are currently the two most widely used bibliometric tools in the field of visualization (Pan et al. 2018 ). In this study, VOSviewer provided the data necessary for the Performance Analysis; Charticulator was then used to redraw using the tabular data exported from VOSviewer (for creating the chord diagram of country collaboration); this was to complement the mapping process, while CiteSpace was primarily utilized to generate keyword maps and conduct burst word analysis.

Data retrieval

This study selected documents from the Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) in the Web of Science Core Collection as the data source, for the following reasons:

(1) The Web of Science Core Collection, as a high-quality digital literature resource database, has been widely accepted by many researchers and is currently considered the most suitable database for bibliometric analysis (Jing et al. 2023a ). Compared to other databases, Web of Science provides more comprehensive data information (Chen et al. 2022a ), and also provides data formats suitable for analysis using VOSviewer and CiteSpace (Gaviria-Marin et al. 2019 ).

(2) The application of digital technology in the field of education is an interdisciplinary research topic, involving technical knowledge literature belonging to the natural sciences and education-related literature belonging to the social sciences. Therefore, it is necessary to select Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) as the sources of research data, ensuring the comprehensiveness of data while ensuring the reliability and persuasiveness of bibliometric research (Hwang and Tsai, 2011 ; Wang et al. 2022 ).

After establishing the source of research data, it is necessary to determine a retrieval strategy (Jing et al. 2023b ). The choice of a retrieval strategy should consider a balance between the breadth and precision of the search formula. That is to say, it should encompass all the literature pertaining to the research topic while excluding irrelevant documents as much as possible. In light of this, this study has set a retrieval strategy informed by multiple related papers (Mustapha et al. 2021 ; Luo et al. 2021 ). The research by Mustapha et al. ( 2021 ) guided us in selecting keywords (“digital” AND “technolog*”) to target digital technology, while Luo et al. ( 2021 ) informed the selection of terms (such as “instruct*,” “teach*,” and “education”) to establish links with the field of education. Then, based on the current application of digital technology in the educational domain and the scope of selection criteria, we constructed the final retrieval strategy. Following the general patterns of past research (Jing et al. 2023a , 2023b ), we conducted a specific screening using the topic search (Topics, TS) function in Web of Science. For the specific criteria used in the screening for this study, please refer to Table 1 .

Literature screening

Literature acquired through keyword searches may contain ostensibly related yet actually unrelated works. Therefore, to ensure the close relevance of literature included in the analysis to the research topic, it is often necessary to perform a manual screening process to identify the final literature to be analyzed, subsequent to completing the initial literature search.

The manual screening process consists of two steps. Initially, irrelevant literature is weeded out based on the title and abstract, with two members of the research team involved in this phase. This stage lasted about one week, resulting in 1106 articles being retained. Subsequently, a comprehensive review of the full text is conducted to accurately identify the literature required for the study. To carry out the second phase of manual screening effectively and scientifically, and to minimize the potential for researcher bias, the research team established the inclusion criteria presented in Table 2 . Three members were engaged in this phase, which took approximately 2 weeks, culminating in the retention of 588 articles after meticulous screening. The entire screening process is depicted in Fig. 1 , adhering to the PRISMA guidelines (Page et al. 2021 ).

The process of obtaining and filtering the necessary literature data for research.

Data standardization

Nguyen and Hallinger ( 2020 ) pointed out that raw data extracted from scientific databases often contains multiple expressions of the same term, and not addressing these synonymous expressions could affect research results in bibliometric analysis. For instance, in the original data, the author list may include “Tsai, C. C.” and “Tsai, C.-C.”, while the keyword list may include “professional-development” and “professional development,” which often require merging. Therefore, before analyzing the selected literature, a data disambiguation process is necessary to standardize the data (Strotmann and Zhao, 2012 ; Van Eck and Waltman, 2019 ). This study adopted the data standardization process proposed by Taskin and Al ( 2019 ), mainly including the following standardization operations:

Firstly, the author and source fields in the data are corrected and standardized to differentiate authors with similar names.

Secondly, the study checks whether the journals to which the literature belongs have been renamed in the past over 20 years, so as to avoid the influence of periodical name change on the analysis results.

Finally, the keyword field is standardized by unifying parts of speech and singular/plural forms of keywords, which can help eliminate redundant entries in the knowledge graph.

Performance analysis (RQ1)

This section offers a thorough and detailed analysis of the state of research in the field of digital technology education. By utilizing descriptive statistics and visual maps, it provides a comprehensive overview of the development trends, authors, countries, institutions, and journal distribution within the field. The insights presented in this section are of great significance in advancing our understanding of the current state of research in this field and identifying areas for further investigation. The use of visual aids to display inter-country cooperation and the evolution of the field adds to the clarity and coherence of the analysis.

Time trend of the publications

To understand a research field, it is first necessary to understand the most basic quantitative information, among which the change in the number of publications per year best reflects the development trend of a research field. Figure 2 shows the distribution of publication dates.

Time trend of the publications on application of digital technology in education.

From the Fig. 2 , it can be seen that the development of this field over the past over 20 years can be roughly divided into three stages. The first stage was from 2000 to 2007, during which the number of publications was relatively low. Due to various factors such as technological maturity, the academic community did not pay widespread attention to the role of digital technology in expanding the scope of teaching and learning. The second stage was from 2008 to 2019, during which the overall number of publications showed an upward trend, and the development of the field entered an accelerated period, attracting more and more scholars’ attention. The third stage was from 2020 to 2022, during which the number of publications stabilized at around 100. During this period, the impact of the pandemic led to a large number of scholars focusing on the role of digital technology in education during the pandemic, and research on the application of digital technology in education became a core topic in social science research.

Analysis of authors

An analysis of the author’s publication volume provides information about the representative scholars and core research strengths of a research area. Table 3 presents information on the core authors in adaptive learning research, including name, publication number, and average number of citations per article (based on the analysis and statistics from VOSviewer).

Variations in research foci among scholars abound. Within the field of digital technology education application research over the past two decades, Neil Selwyn stands as the most productive author, having published 15 papers garnering a total of 1027 citations, resulting in an average of 68.47 citations per paper. As a Professor at the Faculty of Education at Monash University, Selwyn concentrates on exploring the application of digital technology in higher education contexts (Selwyn et al. 2021 ), as well as related products in higher education such as Coursera, edX, and Udacity MOOC platforms (Bulfin et al. 2014 ). Selwyn’s contributions to the educational sociology perspective include extensive research on the impact of digital technology on education, highlighting the spatiotemporal extension of educational processes and practices through technological means as the greatest value of educational technology (Selwyn, 2012 ; Selwyn and Facer, 2014 ). In addition, he provides a blueprint for the development of future schools in 2030 based on the present impact of digital technology on education (Selwyn et al. 2019 ). The second most productive author in this field, Henderson, also offers significant contributions to the understanding of the important value of digital technology in education, specifically in the higher education setting, with a focus on the impact of the pandemic (Henderson et al. 2015 ; Cohen et al. 2022 ). In contrast, Edwards’ research interests focus on early childhood education, particularly the application of digital technology in this context (Edwards, 2013 ; Bird and Edwards, 2015 ). Additionally, on the technical level, Edwards also mainly prefers digital game technology, because it is a digital technology that children are relatively easy to accept (Edwards, 2015 ).

Analysis of countries/regions and organization

The present study aimed to ascertain the leading countries in digital technology education application research by analyzing 75 countries related to 558 works of literature. Table 4 depicts the top ten countries that have contributed significantly to this field in terms of publication count (based on the analysis and statistics from VOSviewer). Our analysis of Table 4 data shows that England emerged as the most influential country/region, with 92 published papers and 2401 citations. Australia and the United States secured the second and third ranks, respectively, with 90 papers (2187 citations) and 70 papers (1331 citations) published. Geographically, most of the countries featured in the top ten publication volumes are situated in Australia, North America, and Europe, with China being the only exception. Notably, all these countries, except China, belong to the group of developed nations, suggesting that economic strength is a prerequisite for fostering research in the digital technology education application field.

This study presents a visual representation of the publication output and cooperation relationships among different countries in the field of digital technology education application research. Specifically, a chord diagram is employed to display the top 30 countries in terms of publication output, as depicted in Fig. 3 . The chord diagram is composed of nodes and chords, where the nodes are positioned as scattered points along the circumference, and the length of each node corresponds to the publication output, with longer lengths indicating higher publication output. The chords, on the other hand, represent the cooperation relationships between any two countries, and are weighted based on the degree of closeness of the cooperation, with wider chords indicating closer cooperation. Through the analysis of the cooperation relationships, the findings suggest that the main publishing countries in this field are engaged in cooperative relationships with each other, indicating a relatively high level of international academic exchange and research internationalization.

In the diagram, nodes are scattered along the circumference of a circle, with the length of each node representing the volume of publications. The weighted arcs connecting any two points on the circle are known as chords, representing the collaborative relationship between the two, with the width of the arc indicating the closeness of the collaboration.

Further analyzing Fig. 3 , we can extract more valuable information, enabling a deeper understanding of the connections between countries in the research field of digital technology in educational applications. It is evident that certain countries, such as the United States, China, and England, display thicker connections, indicating robust collaborative relationships in terms of productivity. These thicker lines signify substantial mutual contributions and shared objectives in certain sectors or fields, highlighting the interconnectedness and global integration in these areas. By delving deeper, we can also explore potential future collaboration opportunities through the chord diagram, identifying possible partners to propel research and development in this field. In essence, the chord diagram successfully encapsulates and conveys the multi-dimensionality of global productivity and cooperation, allowing for a comprehensive understanding of the intricate inter-country relationships and networks in a global context, providing valuable guidance and insights for future research and collaborations.

An in-depth examination of the publishing institutions is provided in Table 5 , showcasing the foremost 10 institutions ranked by their publication volume. Notably, Monash University and Australian Catholic University, situated in Australia, have recorded the most prolific publications within the digital technology education application realm, with 22 and 10 publications respectively. Moreover, the University of Oslo from Norway is featured among the top 10 publishing institutions, with an impressive average citation count of 64 per publication. It is worth highlighting that six institutions based in the United Kingdom were also ranked within the top 10 publishing institutions, signifying their leading position in this area of research.

Analysis of journals

Journals are the main carriers for publishing high-quality papers. Some scholars point out that the two key factors to measure the influence of journals in the specified field are the number of articles published and the number of citations. The more papers published in a magazine and the more citations, the greater its influence (Dzikowski, 2018 ). Therefore, this study utilized VOSviewer to statistically analyze the top 10 journals with the most publications in the field of digital technology in education and calculated the average citations per article (see Table 6 ).

Based on Table 6 , it is apparent that the highest number of articles in the domain of digital technology in education research were published in Education and Information Technologies (47 articles), Computers & Education (34 articles), and British Journal of Educational Technology (32 articles), indicating a higher article output compared to other journals. This underscores the fact that these three journals concentrate more on the application of digital technology in education. Furthermore, several other journals, such as Technology Pedagogy and Education and Sustainability, have published more than 15 articles in this domain. Sustainability represents the open access movement, which has notably facilitated research progress in this field, indicating that the development of open access journals in recent years has had a significant impact. Although there is still considerable disagreement among scholars on the optimal approach to achieve open access, the notion that research outcomes should be accessible to all is widely recognized (Huang et al. 2020 ). On further analysis of the research fields to which these journals belong, except for Sustainability, it is evident that they all pertain to educational technology, thus providing a qualitative definition of the research area of digital technology education from the perspective of journals.

Temporal keyword analysis: thematic evolution (RQ2)

The evolution of research themes is a dynamic process, and previous studies have attempted to present the developmental trajectory of fields by drawing keyword networks in phases (Kumar et al. 2021 ; Chen et al. 2022b ). To understand the shifts in research topics across different periods, this study follows past research and, based on the significant changes in the research field and corresponding technological advancements during the outlined periods, divides the timeline into four stages (the first stage from January 2000 to December 2005, the second stage from January 2006 to December 2011, the third stage from January 2012 to December 2017; and the fourth stage from January 2018 to December 2022). The division into these four stages was determined through a combination of bibliometric analysis and literature review, which presented a clear trajectory of the field’s development. The research analyzes the keyword networks for each time period (as there are only three articles in the first stage, it was not possible to generate an appropriate keyword co-occurrence map, hence only the keyword co-occurrence maps from the second to the fourth stages are provided), to understand the evolutionary track of the digital technology education application research field over time.

2000.1–2005.12: germination period

From January 2000 to December 2005, digital technology education application research was in its infancy. Only three studies focused on digital technology, all of which were related to computers. Due to the popularity of computers, the home became a new learning environment, highlighting the important role of digital technology in expanding the scope of learning spaces (Sutherland et al. 2000 ). In specific disciplines and contexts, digital technology was first favored in medical clinical practice, becoming an important tool for supporting the learning of clinical knowledge and practice (Tegtmeyer et al. 2001 ; Durfee et al. 2003 ).

2006.1–2011.12: initial development period

Between January 2006 and December 2011, it was the initial development period of digital technology education research. Significant growth was observed in research related to digital technology, and discussions and theoretical analyses about “digital natives” emerged. During this phase, scholars focused on the debate about “how to use digital technology reasonably” and “whether current educational models and school curriculum design need to be adjusted on a large scale” (Bennett and Maton, 2010 ; Selwyn, 2009 ; Margaryan et al. 2011 ). These theoretical and speculative arguments provided a unique perspective on the impact of cognitive digital technology on education and teaching. As can be seen from the vocabulary such as “rethinking”, “disruptive pedagogy”, and “attitude” in Fig. 4 , many scholars joined the calm reflection and analysis under the trend of digital technology (Laurillard, 2008 ; Vratulis et al. 2011 ). During this phase, technology was still undergoing dramatic changes. The development of mobile technology had already caught the attention of many scholars (Wong et al. 2011 ), but digital technology represented by computers was still very active (Selwyn et al. 2011 ). The change in technological form would inevitably lead to educational transformation. Collins and Halverson ( 2010 ) summarized the prospects and challenges of using digital technology for learning and educational practices, believing that digital technology would bring a disruptive revolution to the education field and bring about a new educational system. In addition, the term “teacher education” in Fig. 4 reflects the impact of digital technology development on teachers. The rapid development of technology has widened the generation gap between teachers and students. To ensure smooth communication between teachers and students, teachers must keep up with the trend of technological development and establish a lifelong learning concept (Donnison, 2009 ).

In the diagram, each node represents a keyword, with the size of the node indicating the frequency of occurrence of the keyword. The connections represent the co-occurrence relationships between keywords, with a higher frequency of co-occurrence resulting in tighter connections.

2012.1–2017.12: critical exploration period

During the period spanning January 2012 to December 2017, the application of digital technology in education research underwent a significant exploration phase. As can be seen from Fig. 5 , different from the previous stage, the specific elements of specific digital technology have started to increase significantly, including the enrichment of technological contexts, the greater variety of research methods, and the diversification of learning modes. Moreover, the temporal and spatial dimensions of the learning environment were further de-emphasized, as noted in previous literature (Za et al. 2014 ). Given the rapidly accelerating pace of technological development, the education system in the digital era is in urgent need of collaborative evolution and reconstruction, as argued by Davis, Eickelmann, and Zaka ( 2013 ).

In the domain of digital technology, social media has garnered substantial scholarly attention as a promising avenue for learning, as noted by Pasquini and Evangelopoulos ( 2016 ). The implementation of social media in education presents several benefits, including the liberation of education from the restrictions of physical distance and time, as well as the erasure of conventional educational boundaries. The user-generated content (UGC) model in social media has emerged as a crucial source for knowledge creation and distribution, with the widespread adoption of mobile devices. Moreover, social networks have become an integral component of ubiquitous learning environments (Hwang et al. 2013 ). The utilization of social media allows individuals to function as both knowledge producers and recipients, which leads to a blurring of the conventional roles of learners and teachers. On mobile platforms, the roles of learners and teachers are not fixed, but instead interchangeable.

In terms of research methodology, the prevalence of empirical studies with survey designs in the field of educational technology during this period is evident from the vocabulary used, such as “achievement,” “acceptance,” “attitude,” and “ict.” in Fig. 5 . These studies aim to understand learners’ willingness to adopt and attitudes towards new technologies, and some seek to investigate the impact of digital technologies on learning outcomes through quasi-experimental designs (Domínguez et al. 2013 ). Among these empirical studies, mobile learning emerged as a hot topic, and this is not surprising. First, the advantages of mobile learning environments over traditional ones have been empirically demonstrated (Hwang et al. 2013 ). Second, learners born around the turn of the century have been heavily influenced by digital technologies and have developed their own learning styles that are more open to mobile devices as a means of learning. Consequently, analyzing mobile learning as a relatively novel mode of learning has become an important issue for scholars in the field of educational technology.

The intervention of technology has led to the emergence of several novel learning modes, with the blended learning model being the most representative one in the current phase. Blended learning, a novel concept introduced in the information age, emphasizes the integration of the benefits of traditional learning methods and online learning. This learning mode not only highlights the prominent role of teachers in guiding, inspiring, and monitoring the learning process but also underlines the importance of learners’ initiative, enthusiasm, and creativity in the learning process. Despite being an early conceptualization, blended learning’s meaning has been expanded by the widespread use of mobile technology and social media in education. The implementation of new technologies, particularly mobile devices, has resulted in the transformation of curriculum design and increased flexibility and autonomy in students’ learning processes (Trujillo Maza et al. 2016 ), rekindling scholarly attention to this learning mode. However, some scholars have raised concerns about the potential drawbacks of the blended learning model, such as its significant impact on the traditional teaching system, the lack of systematic coping strategies and relevant policies in several schools and regions (Moskal et al. 2013 ).

2018.1–2022.12: accelerated transformation period

The period spanning from January 2018 to December 2022 witnessed a rapid transformation in the application of digital technology in education research. The field of digital technology education research reached a peak period of publication, largely influenced by factors such as the COVID-19 pandemic (Yu et al. 2023 ). Research during this period was built upon the achievements, attitudes, and social media of the previous phase, and included more elements that reflect the characteristics of this research field, such as digital literacy, digital competence, and professional development, as depicted in Fig. 6 . Alongside this, scholars’ expectations for the value of digital technology have expanded, and the pursuit of improving learning efficiency and performance is no longer the sole focus. Some research now aims to cultivate learners’ motivation and enhance their self-efficacy by applying digital technology in a reasonable manner, as demonstrated by recent studies (Beardsley et al. 2021 ; Creely et al. 2021 ).

The COVID-19 pandemic has emerged as a crucial backdrop for the digital technology’s role in sustaining global education, as highlighted by recent scholarly research (Zhou et al. 2022 ; Pan and Zhang, 2020 ; Mo et al. 2022 ). The online learning environment, which is supported by digital technology, has become the primary battleground for global education (Yu, 2022 ). This social context has led to various studies being conducted, with some scholars positing that the pandemic has impacted the traditional teaching order while also expanding learning possibilities in terms of patterns and forms (Alabdulaziz, 2021 ). Furthermore, the pandemic has acted as a catalyst for teacher teaching and technological innovation, and this viewpoint has been empirically substantiated (Moorhouse and Wong, 2021 ). Additionally, some scholars believe that the pandemic’s push is a crucial driving force for the digital transformation of the education system, serving as an essential mechanism for overcoming the system’s inertia (Romero et al. 2021 ).

The rapid outbreak of the pandemic posed a challenge to the large-scale implementation of digital technologies, which was influenced by a complex interplay of subjective and objective factors. Objective constraints included the lack of infrastructure in some regions to support digital technologies, while subjective obstacles included psychological resistance among certain students and teachers (Moorhouse, 2021 ). These factors greatly impacted the progress of online learning during the pandemic. Additionally, Timotheou et al. ( 2023 ) conducted a comprehensive systematic review of existing research on digital technology use during the pandemic, highlighting the critical role played by various factors such as learners’ and teachers’ digital skills, teachers’ personal attributes and professional development, school leadership and management, and administration in facilitating the digitalization and transformation of schools.

The current stage of research is characterized by the pivotal term “digital literacy,” denoting a growing interest in learners’ attitudes and adoption of emerging technologies. Initially, the term “literacy” was restricted to fundamental abilities and knowledge associated with books and print materials (McMillan, 1996 ). However, with the swift advancement of computers and digital technology, there have been various attempts to broaden the scope of literacy beyond its traditional meaning, including game literacy (Buckingham and Burn, 2007 ), information literacy (Eisenberg, 2008 ), and media literacy (Turin and Friesem, 2020 ). Similarly, digital literacy has emerged as a crucial concept, and Gilster and Glister ( 1997 ) were the first to introduce this concept, referring to the proficiency in utilizing technology and processing digital information in academic, professional, and daily life settings. In practical educational settings, learners who possess higher digital literacy often exhibit an aptitude for quickly mastering digital devices and applying them intelligently to education and teaching (Yu, 2022 ).

The utilization of digital technology in education has undergone significant changes over the past two decades, and has been a crucial driver of educational reform with each new technological revolution. The impact of these changes on the underlying logic of digital technology education applications has been noticeable. From computer technology to more recent developments such as virtual reality (VR), augmented reality (AR), and artificial intelligence (AI), the acceleration in digital technology development has been ongoing. Educational reforms spurred by digital technology development continue to be dynamic, as each new digital innovation presents new possibilities and models for teaching practice. This is especially relevant in the post-pandemic era, where the importance of technological progress in supporting teaching cannot be overstated (Mughal et al. 2022 ). Existing digital technologies have already greatly expanded the dimensions of education in both time and space, while future digital technologies aim to expand learners’ perceptions. Researchers have highlighted the potential of integrated technology and immersive technology in the development of the educational metaverse, which is highly anticipated to create a new dimension for the teaching and learning environment, foster a new value system for the discipline of educational technology, and more effectively and efficiently achieve the grand educational blueprint of the United Nations’ Sustainable Development Goals (Zhang et al. 2022 ; Li and Yu, 2023 ).

Hotspot evolution analysis (RQ3)

The examination of keyword evolution reveals a consistent trend in the advancement of digital technology education application research. The emergence and transformation of keywords serve as indicators of the varying research interests in this field. Thus, the utilization of the burst detection function available in CiteSpace allowed for the identification of the top 10 burst words that exhibited a high level of burst strength. This outcome is illustrated in Table 7 .

According to the results presented in Table 7 , the explosive terminology within the realm of digital technology education research has exhibited a concentration mainly between the years 2018 and 2022. Prior to this time frame, the emerging keywords were limited to “information technology” and “computer”. Notably, among them, computer, as an emergent keyword, has always had a high explosive intensity from 2008 to 2018, which reflects the important position of computer in digital technology and is the main carrier of many digital technologies such as Learning Management Systems (LMS) and Assessment and Feedback systems (Barlovits et al. 2022 ).

Since 2018, an increasing number of research studies have focused on evaluating the capabilities of learners to accept, apply, and comprehend digital technologies. As indicated by the use of terms such as “digital literacy” and “digital skill,” the assessment of learners’ digital literacy has become a critical task. Scholarly efforts have been directed towards the development of literacy assessment tools and the implementation of empirical assessments. Furthermore, enhancing the digital literacy of both learners and educators has garnered significant attention. (Nagle, 2018 ; Yu, 2022 ). Simultaneously, given the widespread use of various digital technologies in different formal and informal learning settings, promoting learners’ digital skills has become a crucial objective for contemporary schools (Nygren et al. 2019 ; Forde and OBrien, 2022 ).

Since 2020, the field of applied research on digital technology education has witnessed the emergence of three new hotspots, all of which have been affected to some extent by the pandemic. Firstly, digital technology has been widely applied in physical education, which is one of the subjects that has been severely affected by the pandemic (Parris et al. 2022 ; Jiang and Ning, 2022 ). Secondly, digital transformation has become an important measure for most schools, especially higher education institutions, to cope with the impact of the pandemic globally (García-Morales et al. 2021 ). Although the concept of digital transformation was proposed earlier, the COVID-19 pandemic has greatly accelerated this transformation process. Educational institutions must carefully redesign their educational products to face this new situation, providing timely digital learning methods, environments, tools, and support systems that have far-reaching impacts on modern society (Krishnamurthy, 2020 ; Salas-Pilco et al. 2022 ). Moreover, the professional development of teachers has become a key mission of educational institutions in the post-pandemic era. Teachers need to have a certain level of digital literacy and be familiar with the tools and online teaching resources used in online teaching, which has become a research hotspot today. Organizing digital skills training for teachers to cope with the application of emerging technologies in education is an important issue for teacher professional development and lifelong learning (Garzón-Artacho et al. 2021 ). As the main organizers and practitioners of emergency remote teaching (ERT) during the pandemic, teachers must put cognitive effort into their professional development to ensure effective implementation of ERT (Romero-Hall and Jaramillo Cherrez, 2022 ).

The burst word “digital transformation” reveals that we are in the midst of an ongoing digital technology revolution. With the emergence of innovative digital technologies such as ChatGPT and Microsoft 365 Copilot, technology trends will continue to evolve, albeit unpredictably. While the impact of these advancements on school education remains uncertain, it is anticipated that the widespread integration of technology will significantly affect the current education system. Rejecting emerging technologies without careful consideration is unwise. Like any revolution, the technological revolution in the education field has both positive and negative aspects. Detractors argue that digital technology disrupts learning and memory (Baron, 2021 ) or causes learners to become addicted and distracted from learning (Selwyn and Aagaard, 2020 ). On the other hand, the prudent use of digital technology in education offers a glimpse of a golden age of open learning. Educational leaders and practitioners have the opportunity to leverage cutting-edge digital technologies to address current educational challenges and develop a rational path for the sustainable and healthy growth of education.

Discussion on performance analysis (RQ1)

The field of digital technology education application research has experienced substantial growth since the turn of the century, a phenomenon that is quantifiably apparent through an analysis of authorship, country/region contributions, and institutional engagement. This expansion reflects the increased integration of digital technologies in educational settings and the heightened scholarly interest in understanding and optimizing their use.

Discussion on authorship productivity in digital technology education research

The authorship distribution within digital technology education research is indicative of the field’s intellectual structure and depth. A primary figure in this domain is Neil Selwyn, whose substantial citation rate underscores the profound impact of his work. His focus on the implications of digital technology in higher education and educational sociology has proven to be seminal. Selwyn’s research trajectory, especially the exploration of spatiotemporal extensions of education through technology, provides valuable insights into the multifaceted role of digital tools in learning processes (Selwyn et al. 2019 ).

Other notable contributors, like Henderson and Edwards, present diversified research interests, such as the impact of digital technologies during the pandemic and their application in early childhood education, respectively. Their varied focuses highlight the breadth of digital technology education research, encompassing pedagogical innovation, technological adaptation, and policy development.

Discussion on country/region-level productivity and collaboration

At the country/region level, the United Kingdom, specifically England, emerges as a leading contributor with 92 published papers and a significant citation count. This is closely followed by Australia and the United States, indicating a strong English-speaking research axis. Such geographical concentration of scholarly output often correlates with investment in research and development, technological infrastructure, and the prevalence of higher education institutions engaging in cutting-edge research.

China’s notable inclusion as the only non-Western country among the top contributors to the field suggests a growing research capacity and interest in digital technology in education. However, the lower average citation per paper for China could reflect emerging engagement or different research focuses that may not yet have achieved the same international recognition as Western counterparts.

The chord diagram analysis furthers this understanding, revealing dense interconnections between countries like the United States, China, and England, which indicates robust collaborations. Such collaborations are fundamental in addressing global educational challenges and shaping international research agendas.

Discussion on institutional-level contributions to digital technology education

Institutional productivity in digital technology education research reveals a constellation of universities driving the field forward. Monash University and the Australian Catholic University have the highest publication output, signaling Australia’s significant role in advancing digital education research. The University of Oslo’s remarkable average citation count per publication indicates influential research contributions, potentially reflecting high-quality studies that resonate with the broader academic community.

The strong showing of UK institutions, including the University of London, The Open University, and the University of Cambridge, reinforces the UK’s prominence in this research field. Such institutions are often at the forefront of pedagogical innovation, benefiting from established research cultures and funding mechanisms that support sustained inquiry into digital education.

Discussion on journal publication analysis

An examination of journal outputs offers a lens into the communicative channels of the field’s knowledge base. Journals such as Education and Information Technologies , Computers & Education , and the British Journal of Educational Technology not only serve as the primary disseminators of research findings but also as indicators of research quality and relevance. The impact factor (IF) serves as a proxy for the quality and influence of these journals within the academic community.

The high citation counts for articles published in Computers & Education suggest that research disseminated through this medium has a wide-reaching impact and is of particular interest to the field. This is further evidenced by its significant IF of 11.182, indicating that the journal is a pivotal platform for seminal work in the application of digital technology in education.

The authorship, regional, and institutional productivity in the field of digital technology education application research collectively narrate the evolution of this domain since the turn of the century. The prominence of certain authors and countries underscores the importance of socioeconomic factors and existing academic infrastructure in fostering research productivity. Meanwhile, the centrality of specific journals as outlets for high-impact research emphasizes the role of academic publishing in shaping the research landscape.

As the field continues to grow, future research may benefit from leveraging the collaborative networks that have been elucidated through this analysis, perhaps focusing on underrepresented regions to broaden the scope and diversity of research. Furthermore, the stabilization of publication numbers in recent years invites a deeper exploration into potential plateaus in research trends or saturation in certain sub-fields, signaling an opportunity for novel inquiries and methodological innovations.

Discussion on the evolutionary trends (RQ2)

The evolution of the research field concerning the application of digital technology in education over the past two decades is a story of convergence, diversification, and transformation, shaped by rapid technological advancements and shifting educational paradigms.

At the turn of the century, the inception of digital technology in education was largely exploratory, with a focus on how emerging computer technologies could be harnessed to enhance traditional learning environments. Research from this early period was primarily descriptive, reflecting on the potential and challenges of incorporating digital tools into the educational setting. This phase was critical in establishing the fundamental discourse that would guide subsequent research, as it set the stage for understanding the scope and impact of digital technology in learning spaces (Wang et al. 2023 ).

As the first decade progressed, the narrative expanded to encompass the pedagogical implications of digital technologies. This was a period of conceptual debates, where terms like “digital natives” and “disruptive pedagogy” entered the academic lexicon, underscoring the growing acknowledgment of digital technology as a transformative force within education (Bennett and Maton, 2010 ). During this time, the research began to reflect a more nuanced understanding of the integration of technology, considering not only its potential to change where and how learning occurred but also its implications for educational equity and access.

In the second decade, with the maturation of internet connectivity and mobile technology, the focus of research shifted from theoretical speculations to empirical investigations. The proliferation of digital devices and the ubiquity of social media influenced how learners interacted with information and each other, prompting a surge in studies that sought to measure the impact of these tools on learning outcomes. The digital divide and issues related to digital literacy became central concerns, as scholars explored the varying capacities of students and educators to engage with technology effectively.

Throughout this period, there was an increasing emphasis on the individualization of learning experiences, facilitated by adaptive technologies that could cater to the unique needs and pacing of learners (Jing et al. 2023a ). This individualization was coupled with a growing recognition of the importance of collaborative learning, both online and offline, and the role of digital tools in supporting these processes. Blended learning models, which combined face-to-face instruction with online resources, emerged as a significant trend, advocating for a balance between traditional pedagogies and innovative digital strategies.

The later years, particularly marked by the COVID-19 pandemic, accelerated the necessity for digital technology in education, transforming it from a supplementary tool to an essential platform for delivering education globally (Mo et al. 2022 ; Mustapha et al. 2021 ). This era brought about an unprecedented focus on online learning environments, distance education, and virtual classrooms. Research became more granular, examining not just the pedagogical effectiveness of digital tools, but also their role in maintaining continuity of education during crises, their impact on teacher and student well-being, and their implications for the future of educational policy and infrastructure.

Across these two decades, the research field has seen a shift from examining digital technology as an external addition to the educational process, to viewing it as an integral component of curriculum design, instructional strategies, and even assessment methods. The emergent themes have broadened from a narrow focus on specific tools or platforms to include wider considerations such as data privacy, ethical use of technology, and the environmental impact of digital tools.

Moreover, the field has moved from considering the application of digital technology in education as a primarily cognitive endeavor to recognizing its role in facilitating socio-emotional learning, digital citizenship, and global competencies. Researchers have increasingly turned their attention to the ways in which technology can support collaborative skills, cultural understanding, and ethical reasoning within diverse student populations.

In summary, the past over twenty years in the research field of digital technology applications in education have been characterized by a progression from foundational inquiries to complex analyses of digital integration. This evolution has mirrored the trajectory of technology itself, from a facilitative tool to a pervasive ecosystem defining contemporary educational experiences. As we look to the future, the field is poised to delve into the implications of emerging technologies like AI, AR, and VR, and their potential to redefine the educational landscape even further. This ongoing metamorphosis suggests that the application of digital technology in education will continue to be a rich area of inquiry, demanding continual adaptation and forward-thinking from educators and researchers alike.

Discussion on the study of research hotspots (RQ3)

The analysis of keyword evolution in digital technology education application research elucidates the current frontiers in the field, reflecting a trajectory that is in tandem with the rapidly advancing digital age. This landscape is sculpted by emergent technological innovations and shaped by the demands of an increasingly digital society.

Interdisciplinary integration and pedagogical transformation

One of the frontiers identified from recent keyword bursts includes the integration of digital technology into diverse educational contexts, particularly noted with the keyword “physical education.” The digitalization of disciplines traditionally characterized by physical presence illustrates the pervasive reach of technology and signifies a push towards interdisciplinary integration where technology is not only a facilitator but also a transformative agent. This integration challenges educators to reconceptualize curriculum delivery to accommodate digital tools that can enhance or simulate the physical aspects of learning.

Digital literacy and skills acquisition

Another pivotal frontier is the focus on “digital literacy” and “digital skill”, which has intensified in recent years. This suggests a shift from mere access to technology towards a comprehensive understanding and utilization of digital tools. In this realm, the emphasis is not only on the ability to use technology but also on critical thinking, problem-solving, and the ethical use of digital resources (Yu, 2022 ). The acquisition of digital literacy is no longer an additive skill but a fundamental aspect of modern education, essential for navigating and contributing to the digital world.

Educational digital transformation

The keyword “digital transformation” marks a significant research frontier, emphasizing the systemic changes that education institutions must undergo to align with the digital era (Romero et al. 2021 ). This transformation includes the redesigning of learning environments, pedagogical strategies, and assessment methods to harness digital technology’s full potential. Research in this area explores the complexity of institutional change, addressing the infrastructural, cultural, and policy adjustments needed for a seamless digital transition.

Engagement and participation

Further exploration into “engagement” and “participation” underscores the importance of student-centered learning environments that are mediated by technology. The current frontiers examine how digital platforms can foster collaboration, inclusivity, and active learning, potentially leading to more meaningful and personalized educational experiences. Here, the use of technology seeks to support the emotional and cognitive aspects of learning, moving beyond the transactional view of education to one that is relational and interactive.

Professional development and teacher readiness

As the field evolves, “professional development” emerges as a crucial area, particularly in light of the pandemic which necessitated emergency remote teaching. The need for teacher readiness in a digital age is a pressing frontier, with research focusing on the competencies required for educators to effectively integrate technology into their teaching practices. This includes familiarity with digital tools, pedagogical innovation, and an ongoing commitment to personal and professional growth in the digital domain.

Pandemic as a catalyst

The recent pandemic has acted as a catalyst for accelerated research and application in this field, particularly in the domains of “digital transformation,” “professional development,” and “physical education.” This period has been a litmus test for the resilience and adaptability of educational systems to continue their operations in an emergency. Research has thus been directed at understanding how digital technologies can support not only continuity but also enhance the quality and reach of education in such contexts.

Ethical and societal considerations

The frontier of digital technology in education is also expanding to consider broader ethical and societal implications. This includes issues of digital equity, data privacy, and the sociocultural impact of technology on learning communities. The research explores how educational technology can be leveraged to address inequities and create more equitable learning opportunities for all students, regardless of their socioeconomic background.

Innovation and emerging technologies

Looking forward, the frontiers are set to be influenced by ongoing and future technological innovations, such as artificial intelligence (AI) (Wu and Yu, 2023 ; Chen et al. 2022a ). The exploration into how these technologies can be integrated into educational practices to create immersive and adaptive learning experiences represents a bold new chapter for the field.

In conclusion, the current frontiers of research on the application of digital technology in education are multifaceted and dynamic. They reflect an overarching movement towards deeper integration of technology in educational systems and pedagogical practices, where the goals are not only to facilitate learning but to redefine it. As these frontiers continue to expand and evolve, they will shape the educational landscape, requiring a concerted effort from researchers, educators, policymakers, and technologists to navigate the challenges and harness the opportunities presented by the digital revolution in education.

Conclusions and future research

Conclusions.

The utilization of digital technology in education is a research area that cuts across multiple technical and educational domains and continues to experience dynamic growth due to the continuous progress of technology. In this study, a systematic review of this field was conducted through bibliometric techniques to examine its development trajectory. The primary focus of the review was to investigate the leading contributors, productive national institutions, significant publications, and evolving development patterns. The study’s quantitative analysis resulted in several key conclusions that shed light on this research field’s current state and future prospects.

(1) The research field of digital technology education applications has entered a stage of rapid development, particularly in recent years due to the impact of the pandemic, resulting in a peak of publications. Within this field, several key authors (Selwyn, Henderson, Edwards, etc.) and countries/regions (England, Australia, USA, etc.) have emerged, who have made significant contributions. International exchanges in this field have become frequent, with a high degree of internationalization in academic research. Higher education institutions in the UK and Australia are the core productive forces in this field at the institutional level.

(2) Education and Information Technologies , Computers & Education , and the British Journal of Educational Technology are notable journals that publish research related to digital technology education applications. These journals are affiliated with the research field of educational technology and provide effective communication platforms for sharing digital technology education applications.

(3) Over the past two decades, research on digital technology education applications has progressed from its early stages of budding, initial development, and critical exploration to accelerated transformation, and it is currently approaching maturity. Technological progress and changes in the times have been key driving forces for educational transformation and innovation, and both have played important roles in promoting the continuous development of education.

(4) Influenced by the pandemic, three emerging frontiers have emerged in current research on digital technology education applications, which are physical education, digital transformation, and professional development under the promotion of digital technology. These frontier research hotspots reflect the core issues that the education system faces when encountering new technologies. The evolution of research hotspots shows that technology breakthroughs in education’s original boundaries of time and space create new challenges. The continuous self-renewal of education is achieved by solving one hotspot problem after another.

The present study offers significant practical implications for scholars and practitioners in the field of digital technology education applications. Firstly, it presents a well-defined framework of the existing research in this area, serving as a comprehensive guide for new entrants to the field and shedding light on the developmental trajectory of this research domain. Secondly, the study identifies several contemporary research hotspots, thus offering a valuable decision-making resource for scholars aiming to explore potential research directions. Thirdly, the study undertakes an exhaustive analysis of published literature to identify core journals in the field of digital technology education applications, with Sustainability being identified as a promising open access journal that publishes extensively on this topic. This finding can potentially facilitate scholars in selecting appropriate journals for their research outputs.

Limitation and future research

Influenced by some objective factors, this study also has some limitations. First of all, the bibliometrics analysis software has high standards for data. In order to ensure the quality and integrity of the collected data, the research only selects the periodical papers in SCIE and SSCI indexes, which are the core collection of Web of Science database, and excludes other databases, conference papers, editorials and other publications, which may ignore some scientific research and original opinions in the field of digital technology education and application research. In addition, although this study used professional software to carry out bibliometric analysis and obtained more objective quantitative data, the analysis and interpretation of data will inevitably have a certain subjective color, and the influence of subjectivity on data analysis cannot be completely avoided. As such, future research endeavors will broaden the scope of literature screening and proactively engage scholars in the field to gain objective and state-of-the-art insights, while minimizing the adverse impact of personal subjectivity on research analysis.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/F9QMHY

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Acknowledgements

This research was supported by the Zhejiang Provincial Social Science Planning Project, “Mechanisms and Pathways for Empowering Classroom Teaching through Learning Spaces under the Strategy of High-Quality Education Development”, the 2022 National Social Science Foundation Education Youth Project “Research on the Strategy of Creating Learning Space Value and Empowering Classroom Teaching under the background of ‘Double Reduction’” (Grant No. CCA220319) and the National College Student Innovation and Entrepreneurship Training Program of China (Grant No. 202310337023).

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Wang, C., Chen, X., Yu, T. et al. Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit Soc Sci Commun 11 , 256 (2024). https://doi.org/10.1057/s41599-024-02717-y

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REALIZING THE PROMISE:

Leading up to the 75th anniversary of the UN General Assembly, this “Realizing the promise: How can education technology improve learning for all?” publication kicks off the Center for Universal Education’s first playbook in a series to help improve education around the world.

It is intended as an evidence-based tool for ministries of education, particularly in low- and middle-income countries, to adopt and more successfully invest in education technology.

While there is no single education initiative that will achieve the same results everywhere—as school systems differ in learners and educators, as well as in the availability and quality of materials and technologies—an important first step is understanding how technology is used given specific local contexts and needs.

The surveys in this playbook are designed to be adapted to collect this information from educators, learners, and school leaders and guide decisionmakers in expanding the use of technology.  

Introduction

While technology has disrupted most sectors of the economy and changed how we communicate, access information, work, and even play, its impact on schools, teaching, and learning has been much more limited. We believe that this limited impact is primarily due to technology being been used to replace analog tools, without much consideration given to playing to technology’s comparative advantages. These comparative advantages, relative to traditional “chalk-and-talk” classroom instruction, include helping to scale up standardized instruction, facilitate differentiated instruction, expand opportunities for practice, and increase student engagement. When schools use technology to enhance the work of educators and to improve the quality and quantity of educational content, learners will thrive.

Further, COVID-19 has laid bare that, in today’s environment where pandemics and the effects of climate change are likely to occur, schools cannot always provide in-person education—making the case for investing in education technology.

Here we argue for a simple yet surprisingly rare approach to education technology that seeks to:

• Understand the needs, infrastructure, and capacity of a school system—the diagnosis;
• Survey the best available evidence on interventions that match those conditions—the evidence; and
• Closely monitor the results of innovations before they are scaled up—the prognosis.

RELATED CONTENT

Podcast: How education technology can improve learning for all students

To make ed tech work, set clear goals, review the evidence, and pilot before you scale

The framework.

Our approach builds on a simple yet intuitive theoretical framework created two decades ago by two of the most prominent education researchers in the United States, David K. Cohen and Deborah Loewenberg Ball. They argue that what matters most to improve learning is the interactions among educators and learners around educational materials. We believe that the failed school-improvement efforts in the U.S. that motivated Cohen and Ball’s framework resemble the ed-tech reforms in much of the developing world to date in the lack of clarity improving the interactions between educators, learners, and the educational material. We build on their framework by adding parents as key agents that mediate the relationships between learners and educators and the material (Figure 1).

Figure 1: The instructional core

Adapted from Cohen and Ball (1999)

As the figure above suggests, ed-tech interventions can affect the instructional core in a myriad of ways. Yet, just because technology can do something, it does not mean it should. School systems in developing countries differ along many dimensions and each system is likely to have different needs for ed-tech interventions, as well as different infrastructure and capacity to enact such interventions.

The diagnosis:

How can school systems assess their needs and preparedness.

A useful first step for any school system to determine whether it should invest in education technology is to diagnose its:

• Specific needs to improve student learning (e.g., raising the average level of achievement, remediating gaps among low performers, and challenging high performers to develop higher-order skills);
• Infrastructure to adopt technology-enabled solutions (e.g., electricity connection, availability of space and outlets, stock of computers, and Internet connectivity at school and at learners’ homes); and
• Capacity to integrate technology in the instructional process (e.g., learners’ and educators’ level of familiarity and comfort with hardware and software, their beliefs about the level of usefulness of technology for learning purposes, and their current uses of such technology).

Before engaging in any new data collection exercise, school systems should take full advantage of existing administrative data that could shed light on these three main questions. This could be in the form of internal evaluations but also international learner assessments, such as the Program for International Student Assessment (PISA), the Trends in International Mathematics and Science Study (TIMSS), and/or the Progress in International Literacy Study (PIRLS), and the Teaching and Learning International Study (TALIS). But if school systems lack information on their preparedness for ed-tech reforms or if they seek to complement existing data with a richer set of indicators, we developed a set of surveys for learners, educators, and school leaders. Download the full report to see how we map out the main aspects covered by these surveys, in hopes of highlighting how they could be used to inform decisions around the adoption of ed-tech interventions.

The evidence:

How can school systems identify promising ed-tech interventions.

There is no single “ed-tech” initiative that will achieve the same results everywhere, simply because school systems differ in learners and educators, as well as in the availability and quality of materials and technologies. Instead, to realize the potential of education technology to accelerate student learning, decisionmakers should focus on four potential uses of technology that play to its comparative advantages and complement the work of educators to accelerate student learning (Figure 2). These comparative advantages include:

• Scaling up quality instruction, such as through prerecorded quality lessons.
• Facilitating differentiated instruction, through, for example, computer-adaptive learning and live one-on-one tutoring.
• Expanding opportunities to practice.
• Increasing learner engagement through videos and games.

Figure 2: Comparative advantages of technology

Here we review the evidence on ed-tech interventions from 37 studies in 20 countries*, organizing them by comparative advantage. It’s important to note that ours is not the only way to classify these interventions (e.g., video tutorials could be considered as a strategy to scale up instruction or increase learner engagement), but we believe it may be useful to highlight the needs that they could address and why technology is well positioned to do so.

When discussing specific studies, we report the magnitude of the effects of interventions using standard deviations (SDs). SDs are a widely used metric in research to express the effect of a program or policy with respect to a business-as-usual condition (e.g., test scores). There are several ways to make sense of them. One is to categorize the magnitude of the effects based on the results of impact evaluations. In developing countries, effects below 0.1 SDs are considered to be small, effects between 0.1 and 0.2 SDs are medium, and those above 0.2 SDs are large (for reviews that estimate the average effect of groups of interventions, called “meta analyses,” see e.g., Conn, 2017; Kremer, Brannen, & Glennerster, 2013; McEwan, 2014; Snilstveit et al., 2015; Evans & Yuan, 2020.)

*In surveying the evidence, we began by compiling studies from prior general and ed-tech specific evidence reviews that some of us have written and from ed-tech reviews conducted by others. Then, we tracked the studies cited by the ones we had previously read and reviewed those, as well. In identifying studies for inclusion, we focused on experimental and quasi-experimental evaluations of education technology interventions from pre-school to secondary school in low- and middle-income countries that were released between 2000 and 2020. We only included interventions that sought to improve student learning directly (i.e., students’ interaction with the material), as opposed to interventions that have impacted achievement indirectly, by reducing teacher absence or increasing parental engagement. This process yielded 37 studies in 20 countries (see the full list of studies in Appendix B).

Scaling up standardized instruction

One of the ways in which technology may improve the quality of education is through its capacity to deliver standardized quality content at scale. This feature of technology may be particularly useful in three types of settings: (a) those in “hard-to-staff” schools (i.e., schools that struggle to recruit educators with the requisite training and experience—typically, in rural and/or remote areas) (see, e.g., Urquiola & Vegas, 2005); (b) those in which many educators are frequently absent from school (e.g., Chaudhury, Hammer, Kremer, Muralidharan, & Rogers, 2006; Muralidharan, Das, Holla, & Mohpal, 2017); and/or (c) those in which educators have low levels of pedagogical and subject matter expertise (e.g., Bietenbeck, Piopiunik, & Wiederhold, 2018; Bold et al., 2017; Metzler & Woessmann, 2012; Santibañez, 2006) and do not have opportunities to observe and receive feedback (e.g., Bruns, Costa, & Cunha, 2018; Cilliers, Fleisch, Prinsloo, & Taylor, 2018). Technology could address this problem by: (a) disseminating lessons delivered by qualified educators to a large number of learners (e.g., through prerecorded or live lessons); (b) enabling distance education (e.g., for learners in remote areas and/or during periods of school closures); and (c) distributing hardware preloaded with educational materials.

Prerecorded lessons

Technology seems to be well placed to amplify the impact of effective educators by disseminating their lessons. Evidence on the impact of prerecorded lessons is encouraging, but not conclusive. Some initiatives that have used short instructional videos to complement regular instruction, in conjunction with other learning materials, have raised student learning on independent assessments. For example, Beg et al. (2020) evaluated an initiative in Punjab, Pakistan in which grade 8 classrooms received an intervention that included short videos to substitute live instruction, quizzes for learners to practice the material from every lesson, tablets for educators to learn the material and follow the lesson, and LED screens to project the videos onto a classroom screen. After six months, the intervention improved the performance of learners on independent tests of math and science by 0.19 and 0.24 SDs, respectively but had no discernible effect on the math and science section of Punjab’s high-stakes exams.

One study suggests that approaches that are far less technologically sophisticated can also improve learning outcomes—especially, if the business-as-usual instruction is of low quality. For example, Naslund-Hadley, Parker, and Hernandez-Agramonte (2014) evaluated a preschool math program in Cordillera, Paraguay that used audio segments and written materials four days per week for an hour per day during the school day. After five months, the intervention improved math scores by 0.16 SDs, narrowing gaps between low- and high-achieving learners, and between those with and without educators with formal training in early childhood education.

Yet, the integration of prerecorded material into regular instruction has not always been successful. For example, de Barros (2020) evaluated an intervention that combined instructional videos for math and science with infrastructure upgrades (e.g., two “smart” classrooms, two TVs, and two tablets), printed workbooks for students, and in-service training for educators of learners in grades 9 and 10 in Haryana, India (all materials were mapped onto the official curriculum). After 11 months, the intervention negatively impacted math achievement (by 0.08 SDs) and had no effect on science (with respect to business as usual classes). It reduced the share of lesson time that educators devoted to instruction and negatively impacted an index of instructional quality. Likewise, Seo (2017) evaluated several combinations of infrastructure (solar lights and TVs) and prerecorded videos (in English and/or bilingual) for grade 11 students in northern Tanzania and found that none of the variants improved student learning, even when the videos were used. The study reports effects from the infrastructure component across variants, but as others have noted (Muralidharan, Romero, & Wüthrich, 2019), this approach to estimating impact is problematic.

A very similar intervention delivered after school hours, however, had sizeable effects on learners’ basic skills. Chiplunkar, Dhar, and Nagesh (2020) evaluated an initiative in Chennai (the capital city of the state of Tamil Nadu, India) delivered by the same organization as above that combined short videos that explained key concepts in math and science with worksheets, facilitator-led instruction, small groups for peer-to-peer learning, and occasional career counseling and guidance for grade 9 students. These lessons took place after school for one hour, five times a week. After 10 months, it had large effects on learners’ achievement as measured by tests of basic skills in math and reading, but no effect on a standardized high-stakes test in grade 10 or socio-emotional skills (e.g., teamwork, decisionmaking, and communication).

Drawing general lessons from this body of research is challenging for at least two reasons. First, all of the studies above have evaluated the impact of prerecorded lessons combined with several other components (e.g., hardware, print materials, or other activities). Therefore, it is possible that the effects found are due to these additional components, rather than to the recordings themselves, or to the interaction between the two (see Muralidharan, 2017 for a discussion of the challenges of interpreting “bundled” interventions). Second, while these studies evaluate some type of prerecorded lessons, none examines the content of such lessons. Thus, it seems entirely plausible that the direction and magnitude of the effects depends largely on the quality of the recordings (e.g., the expertise of the educator recording it, the amount of preparation that went into planning the recording, and its alignment with best teaching practices).

These studies also raise three important questions worth exploring in future research. One of them is why none of the interventions discussed above had effects on high-stakes exams, even if their materials are typically mapped onto the official curriculum. It is possible that the official curricula are simply too challenging for learners in these settings, who are several grade levels behind expectations and who often need to reinforce basic skills (see Pritchett & Beatty, 2015). Another question is whether these interventions have long-term effects on teaching practices. It seems plausible that, if these interventions are deployed in contexts with low teaching quality, educators may learn something from watching the videos or listening to the recordings with learners. Yet another question is whether these interventions make it easier for schools to deliver instruction to learners whose native language is other than the official medium of instruction.

Distance education

Technology can also allow learners living in remote areas to access education. The evidence on these initiatives is encouraging. For example, Johnston and Ksoll (2017) evaluated a program that broadcasted live instruction via satellite to rural primary school students in the Volta and Greater Accra regions of Ghana. For this purpose, the program also equipped classrooms with the technology needed to connect to a studio in Accra, including solar panels, a satellite modem, a projector, a webcam, microphones, and a computer with interactive software. After two years, the intervention improved the numeracy scores of students in grades 2 through 4, and some foundational literacy tasks, but it had no effect on attendance or classroom time devoted to instruction, as captured by school visits. The authors interpreted these results as suggesting that the gains in achievement may be due to improving the quality of instruction that children received (as opposed to increased instructional time). Naik, Chitre, Bhalla, and Rajan (2019) evaluated a similar program in the Indian state of Karnataka and also found positive effects on learning outcomes, but it is not clear whether those effects are due to the program or due to differences in the groups of students they compared to estimate the impact of the initiative.

In one context (Mexico), this type of distance education had positive long-term effects. Navarro-Sola (2019) took advantage of the staggered rollout of the telesecundarias (i.e., middle schools with lessons broadcasted through satellite TV) in 1968 to estimate its impact. The policy had short-term effects on students’ enrollment in school: For every telesecundaria per 50 children, 10 students enrolled in middle school and two pursued further education. It also had a long-term influence on the educational and employment trajectory of its graduates. Each additional year of education induced by the policy increased average income by nearly 18 percent. This effect was attributable to more graduates entering the labor force and shifting from agriculture and the informal sector. Similarly, Fabregas (2019) leveraged a later expansion of this policy in 1993 and found that each additional telesecundaria per 1,000 adolescents led to an average increase of 0.2 years of education, and a decline in fertility for women, but no conclusive evidence of long-term effects on labor market outcomes.

It is crucial to interpret these results keeping in mind the settings where the interventions were implemented. As we mention above, part of the reason why they have proven effective is that the “counterfactual” conditions for learning (i.e., what would have happened to learners in the absence of such programs) was either to not have access to schooling or to be exposed to low-quality instruction. School systems interested in taking up similar interventions should assess the extent to which their learners (or parts of their learner population) find themselves in similar conditions to the subjects of the studies above. This illustrates the importance of assessing the needs of a system before reviewing the evidence.

Preloaded hardware

Technology also seems well positioned to disseminate educational materials. Specifically, hardware (e.g., desktop computers, laptops, or tablets) could also help deliver educational software (e.g., word processing, reference texts, and/or games). In theory, these materials could not only undergo a quality assurance review (e.g., by curriculum specialists and educators), but also draw on the interactions with learners for adjustments (e.g., identifying areas needing reinforcement) and enable interactions between learners and educators.

In practice, however, most initiatives that have provided learners with free computers, laptops, and netbooks do not leverage any of the opportunities mentioned above. Instead, they install a standard set of educational materials and hope that learners find them helpful enough to take them up on their own. Students rarely do so, and instead use the laptops for recreational purposes—often, to the detriment of their learning (see, e.g., Malamud & Pop-Eleches, 2011). In fact, free netbook initiatives have not only consistently failed to improve academic achievement in math or language (e.g., Cristia et al., 2017), but they have had no impact on learners’ general computer skills (e.g., Beuermann et al., 2015). Some of these initiatives have had small impacts on cognitive skills, but the mechanisms through which those effects occurred remains unclear.

To our knowledge, the only successful deployment of a free laptop initiative was one in which a team of researchers equipped the computers with remedial software. Mo et al. (2013) evaluated a version of the One Laptop per Child (OLPC) program for grade 3 students in migrant schools in Beijing, China in which the laptops were loaded with a remedial software mapped onto the national curriculum for math (similar to the software products that we discuss under “practice exercises” below). After nine months, the program improved math achievement by 0.17 SDs and computer skills by 0.33 SDs. If a school system decides to invest in free laptops, this study suggests that the quality of the software on the laptops is crucial.

To date, however, the evidence suggests that children do not learn more from interacting with laptops than they do from textbooks. For example, Bando, Gallego, Gertler, and Romero (2016) compared the effect of free laptop and textbook provision in 271 elementary schools in disadvantaged areas of Honduras. After seven months, students in grades 3 and 6 who had received the laptops performed on par with those who had received the textbooks in math and language. Further, even if textbooks essentially become obsolete at the end of each school year, whereas laptops can be reloaded with new materials for each year, the costs of laptop provision (not just the hardware, but also the technical assistance, Internet, and training associated with it) are not yet low enough to make them a more cost-effective way of delivering content to learners.

Evidence on the provision of tablets equipped with software is encouraging but limited. For example, de Hoop et al. (2020) evaluated a composite intervention for first grade students in Zambia’s Eastern Province that combined infrastructure (electricity via solar power), hardware (projectors and tablets), and educational materials (lesson plans for educators and interactive lessons for learners, both loaded onto the tablets and mapped onto the official Zambian curriculum). After 14 months, the intervention had improved student early-grade reading by 0.4 SDs, oral vocabulary scores by 0.25 SDs, and early-grade math by 0.22 SDs. It also improved students’ achievement by 0.16 on a locally developed assessment. The multifaceted nature of the program, however, makes it challenging to identify the components that are driving the positive effects. Pitchford (2015) evaluated an intervention that provided tablets equipped with educational “apps,” to be used for 30 minutes per day for two months to develop early math skills among students in grades 1 through 3 in Lilongwe, Malawi. The evaluation found positive impacts in math achievement, but the main study limitation is that it was conducted in a single school.

Facilitating differentiated instruction

Another way in which technology may improve educational outcomes is by facilitating the delivery of differentiated or individualized instruction. Most developing countries massively expanded access to schooling in recent decades by building new schools and making education more affordable, both by defraying direct costs, as well as compensating for opportunity costs (Duflo, 2001; World Bank, 2018). These initiatives have not only rapidly increased the number of learners enrolled in school, but have also increased the variability in learner’ preparation for schooling. Consequently, a large number of learners perform well below grade-based curricular expectations (see, e.g., Duflo, Dupas, & Kremer, 2011; Pritchett & Beatty, 2015). These learners are unlikely to get much from “one-size-fits-all” instruction, in which a single educator delivers instruction deemed appropriate for the middle (or top) of the achievement distribution (Banerjee & Duflo, 2011). Technology could potentially help these learners by providing them with: (a) instruction and opportunities for practice that adjust to the level and pace of preparation of each individual (known as “computer-adaptive learning” (CAL)); or (b) live, one-on-one tutoring.

Computer-adaptive learning

One of the main comparative advantages of technology is its ability to diagnose students’ initial learning levels and assign students to instruction and exercises of appropriate difficulty. No individual educator—no matter how talented—can be expected to provide individualized instruction to all learners in his/her class simultaneously . In this respect, technology is uniquely positioned to complement traditional teaching. This use of technology could help learners master basic skills and help them get more out of schooling.

Although many software products evaluated in recent years have been categorized as CAL, many rely on a relatively coarse level of differentiation at an initial stage (e.g., a diagnostic test) without further differentiation. We discuss these initiatives under the category of “increasing opportunities for practice” below. CAL initiatives complement an initial diagnostic with dynamic adaptation (i.e., at each response or set of responses from learners) to adjust both the initial level of difficulty and rate at which it increases or decreases, depending on whether learners’ responses are correct or incorrect.

Existing evidence on this specific type of programs is highly promising. Most famously, Banerjee et al. (2007) evaluated CAL software in Vadodara, in the Indian state of Gujarat, in which grade 4 students were offered two hours of shared computer time per week before and after school, during which they played games that involved solving math problems. The level of difficulty of such problems adjusted based on students’ answers. This program improved math achievement by 0.35 and 0.47 SDs after one and two years of implementation, respectively. Consistent with the promise of personalized learning, the software improved achievement for all students. In fact, one year after the end of the program, students assigned to the program still performed 0.1 SDs better than those assigned to a business as usual condition. More recently, Muralidharan, et al. (2019) evaluated a “blended learning” initiative in which students in grades 4 through 9 in Delhi, India received 45 minutes of interaction with CAL software for math and language, and 45 minutes of small group instruction before or after going to school. After only 4.5 months, the program improved achievement by 0.37 SDs in math and 0.23 SDs in Hindi. While all learners benefited from the program in absolute terms, the lowest performing learners benefited the most in relative terms, since they were learning very little in school.

We see two important limitations from this body of research. First, to our knowledge, none of these initiatives has been evaluated when implemented during the school day. Therefore, it is not possible to distinguish the effect of the adaptive software from that of additional instructional time. Second, given that most of these programs were facilitated by local instructors, attempts to distinguish the effect of the software from that of the instructors has been mostly based on noncausal evidence. A frontier challenge in this body of research is to understand whether CAL software can increase the effectiveness of school-based instruction by substituting part of the regularly scheduled time for math and language instruction.

Live one-on-one tutoring

Recent improvements in the speed and quality of videoconferencing, as well as in the connectivity of remote areas, have enabled yet another way in which technology can help personalization: live (i.e., real-time) one-on-one tutoring. While the evidence on in-person tutoring is scarce in developing countries, existing studies suggest that this approach works best when it is used to personalize instruction (see, e.g., Banerjee et al., 2007; Banerji, Berry, & Shotland, 2015; Cabezas, Cuesta, & Gallego, 2011).

There are almost no studies on the impact of online tutoring—possibly, due to the lack of hardware and Internet connectivity in low- and middle-income countries. One exception is Chemin and Oledan (2020)’s recent evaluation of an online tutoring program for grade 6 students in Kianyaga, Kenya to learn English from volunteers from a Canadian university via Skype ( videoconferencing software) for one hour per week after school. After 10 months, program beneficiaries performed 0.22 SDs better in a test of oral comprehension, improved their comfort using technology for learning, and became more willing to engage in cross-cultural communication. Importantly, while the tutoring sessions used the official English textbooks and sought in part to help learners with their homework, tutors were trained on several strategies to teach to each learner’s individual level of preparation, focusing on basic skills if necessary. To our knowledge, similar initiatives within a country have not yet been rigorously evaluated.

Expanding opportunities for practice

A third way in which technology may improve the quality of education is by providing learners with additional opportunities for practice. In many developing countries, lesson time is primarily devoted to lectures, in which the educator explains the topic and the learners passively copy explanations from the blackboard. This setup leaves little time for in-class practice. Consequently, learners who did not understand the explanation of the material during lecture struggle when they have to solve homework assignments on their own. Technology could potentially address this problem by allowing learners to review topics at their own pace.

Practice exercises

Technology can help learners get more out of traditional instruction by providing them with opportunities to implement what they learn in class. This approach could, in theory, allow some learners to anchor their understanding of the material through trial and error (i.e., by realizing what they may not have understood correctly during lecture and by getting better acquainted with special cases not covered in-depth in class).

Existing evidence on practice exercises reflects both the promise and the limitations of this use of technology in developing countries. For example, Lai et al. (2013) evaluated a program in Shaanxi, China where students in grades 3 and 5 were required to attend two 40-minute remedial sessions per week in which they first watched videos that reviewed the material that had been introduced in their math lessons that week and then played games to practice the skills introduced in the video. After four months, the intervention improved math achievement by 0.12 SDs. Many other evaluations of comparable interventions have found similar small-to-moderate results (see, e.g., Lai, Luo, Zhang, Huang, & Rozelle, 2015; Lai et al., 2012; Mo et al., 2015; Pitchford, 2015). These effects, however, have been consistently smaller than those of initiatives that adjust the difficulty of the material based on students’ performance (e.g., Banerjee et al., 2007; Muralidharan, et al., 2019). We hypothesize that these programs do little for learners who perform several grade levels behind curricular expectations, and who would benefit more from a review of foundational concepts from earlier grades.

We see two important limitations from this research. First, most initiatives that have been evaluated thus far combine instructional videos with practice exercises, so it is hard to know whether their effects are driven by the former or the latter. In fact, the program in China described above allowed learners to ask their peers whenever they did not understand a difficult concept, so it potentially also captured the effect of peer-to-peer collaboration. To our knowledge, no studies have addressed this gap in the evidence.

Second, most of these programs are implemented before or after school, so we cannot distinguish the effect of additional instructional time from that of the actual opportunity for practice. The importance of this question was first highlighted by Linden (2008), who compared two delivery mechanisms for game-based remedial math software for students in grades 2 and 3 in a network of schools run by a nonprofit organization in Gujarat, India: one in which students interacted with the software during the school day and another one in which students interacted with the software before or after school (in both cases, for three hours per day). After a year, the first version of the program had negatively impacted students’ math achievement by 0.57 SDs and the second one had a null effect. This study suggested that computer-assisted learning is a poor substitute for regular instruction when it is of high quality, as was the case in this well-functioning private network of schools.

In recent years, several studies have sought to remedy this shortcoming. Mo et al. (2014) were among the first to evaluate practice exercises delivered during the school day. They evaluated an initiative in Shaanxi, China in which students in grades 3 and 5 were required to interact with the software similar to the one in Lai et al. (2013) for two 40-minute sessions per week. The main limitation of this study, however, is that the program was delivered during regularly scheduled computer lessons, so it could not determine the impact of substituting regular math instruction. Similarly, Mo et al. (2020) evaluated a self-paced and a teacher-directed version of a similar program for English for grade 5 students in Qinghai, China. Yet, the key shortcoming of this study is that the teacher-directed version added several components that may also influence achievement, such as increased opportunities for teachers to provide students with personalized assistance when they struggled with the material. Ma, Fairlie, Loyalka, and Rozelle (2020) compared the effectiveness of additional time-delivered remedial instruction for students in grades 4 to 6 in Shaanxi, China through either computer-assisted software or using workbooks. This study indicates whether additional instructional time is more effective when using technology, but it does not address the question of whether school systems may improve the productivity of instructional time during the school day by substituting educator-led with computer-assisted instruction.

Increasing learner engagement

Another way in which technology may improve education is by increasing learners’ engagement with the material. In many school systems, regular “chalk and talk” instruction prioritizes time for educators’ exposition over opportunities for learners to ask clarifying questions and/or contribute to class discussions. This, combined with the fact that many developing-country classrooms include a very large number of learners (see, e.g., Angrist & Lavy, 1999; Duflo, Dupas, & Kremer, 2015), may partially explain why the majority of those students are several grade levels behind curricular expectations (e.g., Muralidharan, et al., 2019; Muralidharan & Zieleniak, 2014; Pritchett & Beatty, 2015). Technology could potentially address these challenges by: (a) using video tutorials for self-paced learning and (b) presenting exercises as games and/or gamifying practice.

Video tutorials

Technology can potentially increase learner effort and understanding of the material by finding new and more engaging ways to deliver it. Video tutorials designed for self-paced learning—as opposed to videos for whole class instruction, which we discuss under the category of “prerecorded lessons” above—can increase learner effort in multiple ways, including: allowing learners to focus on topics with which they need more help, letting them correct errors and misconceptions on their own, and making the material appealing through visual aids. They can increase understanding by breaking the material into smaller units and tackling common misconceptions.

In spite of the popularity of instructional videos, there is relatively little evidence on their effectiveness. Yet, two recent evaluations of different versions of the Khan Academy portal, which mainly relies on instructional videos, offer some insight into their impact. First, Ferman, Finamor, and Lima (2019) evaluated an initiative in 157 public primary and middle schools in five cities in Brazil in which the teachers of students in grades 5 and 9 were taken to the computer lab to learn math from the platform for 50 minutes per week. The authors found that, while the intervention slightly improved learners’ attitudes toward math, these changes did not translate into better performance in this subject. The authors hypothesized that this could be due to the reduction of teacher-led math instruction.

More recently, Büchel, Jakob, Kühnhanss, Steffen, and Brunetti (2020) evaluated an after-school, offline delivery of the Khan Academy portal in grades 3 through 6 in 302 primary schools in Morazán, El Salvador. Students in this study received 90 minutes per week of additional math instruction (effectively nearly doubling total math instruction per week) through teacher-led regular lessons, teacher-assisted Khan Academy lessons, or similar lessons assisted by technical supervisors with no content expertise. (Importantly, the first group provided differentiated instruction, which is not the norm in Salvadorian schools). All three groups outperformed both schools without any additional lessons and classrooms without additional lessons in the same schools as the program. The teacher-assisted Khan Academy lessons performed 0.24 SDs better, the supervisor-led lessons 0.22 SDs better, and the teacher-led regular lessons 0.15 SDs better, but the authors could not determine whether the effects across versions were different.

Together, these studies suggest that instructional videos work best when provided as a complement to, rather than as a substitute for, regular instruction. Yet, the main limitation of these studies is the multifaceted nature of the Khan Academy portal, which also includes other components found to positively improve learner achievement, such as differentiated instruction by students’ learning levels. While the software does not provide the type of personalization discussed above, learners are asked to take a placement test and, based on their score, educators assign them different work. Therefore, it is not clear from these studies whether the effects from Khan Academy are driven by its instructional videos or to the software’s ability to provide differentiated activities when combined with placement tests.

Games and gamification

Technology can also increase learner engagement by presenting exercises as games and/or by encouraging learner to play and compete with others (e.g., using leaderboards and rewards)—an approach known as “gamification.” Both approaches can increase learner motivation and effort by presenting learners with entertaining opportunities for practice and by leveraging peers as commitment devices.

There are very few studies on the effects of games and gamification in low- and middle-income countries. Recently, Araya, Arias Ortiz, Bottan, and Cristia (2019) evaluated an initiative in which grade 4 students in Santiago, Chile were required to participate in two 90-minute sessions per week during the school day with instructional math software featuring individual and group competitions (e.g., tracking each learner’s standing in his/her class and tournaments between sections). After nine months, the program led to improvements of 0.27 SDs in the national student assessment in math (it had no spillover effects on reading). However, it had mixed effects on non-academic outcomes. Specifically, the program increased learners’ willingness to use computers to learn math, but, at the same time, increased their anxiety toward math and negatively impacted learners’ willingness to collaborate with peers. Finally, given that one of the weekly sessions replaced regular math instruction and the other one represented additional math instructional time, it is not clear whether the academic effects of the program are driven by the software or the additional time devoted to learning math.

The prognosis:

How can school systems adopt interventions that match their needs.

Here are five specific and sequential guidelines for decisionmakers to realize the potential of education technology to accelerate student learning.

1. Take stock of how your current schools, educators, and learners are engaging with technology .

Carry out a short in-school survey to understand the current practices and potential barriers to adoption of technology (we have included suggested survey instruments in the Appendices); use this information in your decisionmaking process. For example, we learned from conversations with current and former ministers of education from various developing regions that a common limitation to technology use is regulations that hold school leaders accountable for damages to or losses of devices. Another common barrier is lack of access to electricity and Internet, or even the availability of sufficient outlets for charging devices in classrooms. Understanding basic infrastructure and regulatory limitations to the use of education technology is a first necessary step. But addressing these limitations will not guarantee that introducing or expanding technology use will accelerate learning. The next steps are thus necessary.

“In Africa, the biggest limit is connectivity. Fiber is expensive, and we don’t have it everywhere. The continent is creating a digital divide between cities, where there is fiber, and the rural areas.  The [Ghanaian] administration put in schools offline/online technologies with books, assessment tools, and open source materials. In deploying this, we are finding that again, teachers are unfamiliar with it. And existing policies prohibit students to bring their own tablets or cell phones. The easiest way to do it would have been to let everyone bring their own device. But policies are against it.” H.E. Matthew Prempeh, Minister of Education of Ghana, on the need to understand the local context.

2. Consider how the introduction of technology may affect the interactions among learners, educators, and content .

Our review of the evidence indicates that technology may accelerate student learning when it is used to scale up access to quality content, facilitate differentiated instruction, increase opportunities for practice, or when it increases learner engagement. For example, will adding electronic whiteboards to classrooms facilitate access to more quality content or differentiated instruction? Or will these expensive boards be used in the same way as the old chalkboards? Will providing one device (laptop or tablet) to each learner facilitate access to more and better content, or offer students more opportunities to practice and learn? Solely introducing technology in classrooms without additional changes is unlikely to lead to improved learning and may be quite costly. If you cannot clearly identify how the interactions among the three key components of the instructional core (educators, learners, and content) may change after the introduction of technology, then it is probably not a good idea to make the investment. See Appendix A for guidance on the types of questions to ask.

3. Once decisionmakers have a clear idea of how education technology can help accelerate student learning in a specific context, it is important to define clear objectives and goals and establish ways to regularly assess progress and make course corrections in a timely manner .

For instance, is the education technology expected to ensure that learners in early grades excel in foundational skills—basic literacy and numeracy—by age 10? If so, will the technology provide quality reading and math materials, ample opportunities to practice, and engaging materials such as videos or games? Will educators be empowered to use these materials in new ways? And how will progress be measured and adjusted?

4. How this kind of reform is approached can matter immensely for its success.

It is easy to nod to issues of “implementation,” but that needs to be more than rhetorical. Keep in mind that good use of education technology requires thinking about how it will affect learners, educators, and parents. After all, giving learners digital devices will make no difference if they get broken, are stolen, or go unused. Classroom technologies only matter if educators feel comfortable putting them to work. Since good technology is generally about complementing or amplifying what educators and learners already do, it is almost always a mistake to mandate programs from on high. It is vital that technology be adopted with the input of educators and families and with attention to how it will be used. If technology goes unused or if educators use it ineffectually, the results will disappoint—no matter the virtuosity of the technology. Indeed, unused education technology can be an unnecessary expenditure for cash-strapped education systems. This is why surveying context, listening to voices in the field, examining how technology is used, and planning for course correction is essential.

5. It is essential to communicate with a range of stakeholders, including educators, school leaders, parents, and learners .

Technology can feel alien in schools, confuse parents and (especially) older educators, or become an alluring distraction. Good communication can help address all of these risks. Taking care to listen to educators and families can help ensure that programs are informed by their needs and concerns. At the same time, deliberately and consistently explaining what technology is and is not supposed to do, how it can be most effectively used, and the ways in which it can make it more likely that programs work as intended. For instance, if teachers fear that technology is intended to reduce the need for educators, they will tend to be hostile; if they believe that it is intended to assist them in their work, they will be more receptive. Absent effective communication, it is easy for programs to “fail” not because of the technology but because of how it was used. In short, past experience in rolling out education programs indicates that it is as important to have a strong intervention design as it is to have a solid plan to socialize it among stakeholders.

Beyond reopening: A leapfrog moment to transform education?

On September 14, the Center for Universal Education (CUE) will host a webinar to discuss strategies, including around the effective use of education technology, for ensuring resilient schools in the long term and to launch a new education technology playbook “Realizing the promise: How can education technology improve learning for all?”

file-pdf Full Playbook – Realizing the promise: How can education technology improve learning for all? file-pdf References file-pdf Appendix A – Instruments to assess availability and use of technology file-pdf Appendix B – List of reviewed studies file-pdf Appendix C – How may technology affect interactions among students, teachers, and content?

About the Authors

Alejandro j. ganimian, emiliana vegas, frederick m. hess.

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• Published: 15 February 2018

Blended learning: the new normal and emerging technologies

• Charles Dziuban 1 ,
• Charles R. Graham 2 ,
• Patsy D. Moskal   ORCID: orcid.org/0000-0001-6376-839X 1 ,
• Anders Norberg 3 &
• Nicole Sicilia 1  

International Journal of Educational Technology in Higher Education volume  15 , Article number:  3 ( 2018 ) Cite this article

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This study addressed several outcomes, implications, and possible future directions for blended learning (BL) in higher education in a world where information communication technologies (ICTs) increasingly communicate with each other. In considering effectiveness, the authors contend that BL coalesces around access, success, and students’ perception of their learning environments. Success and withdrawal rates for face-to-face and online courses are compared to those for BL as they interact with minority status. Investigation of student perception about course excellence revealed the existence of robust if-then decision rules for determining how students evaluate their educational experiences. Those rules were independent of course modality, perceived content relevance, and expected grade. The authors conclude that although blended learning preceded modern instructional technologies, its evolution will be inextricably bound to contemporary information communication technologies that are approximating some aspects of human thought processes.

Introduction

Blended learning and research issues.

Blended learning (BL), or the integration of face-to-face and online instruction (Graham 2013 ), is widely adopted across higher education with some scholars referring to it as the “new traditional model” (Ross and Gage 2006 , p. 167) or the “new normal” in course delivery (Norberg et al. 2011 , p. 207). However, tracking the accurate extent of its growth has been challenging because of definitional ambiguity (Oliver and Trigwell 2005 ), combined with institutions’ inability to track an innovative practice, that in many instances has emerged organically. One early nationwide study sponsored by the Sloan Consortium (now the Online Learning Consortium) found that 65.2% of participating institutions of higher education (IHEs) offered blended (also termed hybrid ) courses (Allen and Seaman 2003 ). A 2008 study, commissioned by the U.S. Department of Education to explore distance education in the U.S., defined BL as “a combination of online and in-class instruction with reduced in-class seat time for students ” (Lewis and Parsad 2008 , p. 1, emphasis added). Using this definition, the study found that 35% of higher education institutions offered blended courses, and that 12% of the 12.2 million documented distance education enrollments were in blended courses.

The 2017 New Media Consortium Horizon Report found that blended learning designs were one of the short term forces driving technology adoption in higher education in the next 1–2 years (Adams Becker et al. 2017 ). Also, blended learning is one of the key issues in teaching and learning in the EDUCAUSE Learning Initiative’s 2017 annual survey of higher education (EDUCAUSE 2017 ). As institutions begin to examine BL instruction, there is a growing research interest in exploring the implications for both faculty and students. This modality is creating a community of practice built on a singular and pervasive research question, “How is blended learning impacting the teaching and learning environment?” That question continues to gain traction as investigators study the complexities of how BL interacts with cognitive, affective, and behavioral components of student behavior, and examine its transformation potential for the academy. Those issues are so compelling that several volumes have been dedicated to assembling the research on how blended learning can be better understood (Dziuban et al. 2016 ; Picciano et al. 2014 ; Picciano and Dziuban 2007 ; Bonk and Graham 2007 ; Kitchenham 2011 ; Jean-François 2013 ; Garrison and Vaughan 2013 ) and at least one organization, the Online Learning Consortium, sponsored an annual conference solely dedicated to blended learning at all levels of education and training (2004–2015). These initiatives address blended learning in a wide variety of situations. For instance, the contexts range over K-12 education, industrial and military training, conceptual frameworks, transformational potential, authentic assessment, and new research models. Further, many of these resources address students’ access, success, withdrawal, and perception of the degree to which blended learning provides an effective learning environment.

Currently the United States faces a widening educational gap between our underserved student population and those communities with greater financial and technological resources (Williams 2016 ). Equal access to education is a critical need, one that is particularly important for those in our underserved communities. Can blended learning help increase access thereby alleviating some of the issues faced by our lower income students while resulting in improved educational equality? Although most indicators suggest “yes” (Dziuban et al. 2004 ), it seems that, at the moment, the answer is still “to be determined.” Quality education presents a challenge, evidenced by many definitions of what constitutes its fundamental components (Pirsig 1974 ; Arum et al. 2016 ). Although progress has been made by initiatives, such as, Quality Matters ( 2016 ), the OLC OSCQR Course Design Review Scorecard developed by Open SUNY (Open SUNY n.d. ), the Quality Scorecard for Blended Learning Programs (Online Learning Consortium n.d. ), and SERVQUAL (Alhabeeb 2015 ), the issue is by no means resolved. Generally, we still make quality education a perceptual phenomenon where we ascribe that attribute to a course, educational program, or idea, but struggle with precisely why we reached that decision. Searle ( 2015 ), summarizes the problem concisely arguing that quality does not exist independently, but is entirely observer dependent. Pirsig ( 1974 ) in his iconic volume on the nature of quality frames the context this way,

“There is such thing as Quality, but that as soon as you try to define it, something goes haywire. You can’t do it” (p. 91).

Therefore, attempting to formulate a semantic definition of quality education with syntax-based metrics results in what O’Neil (O'Neil 2017 ) terms surrogate models that are rough approximations and oversimplified. Further, the derived metrics tend to morph into goals or benchmarks, losing their original measurement properties (Goodhart 1975 ).

Information communication technologies in society and education

Blended learning forces us to consider the characteristics of digital technology, in general, and information communication technologies (ICTs), more specifically. Floridi ( 2014 ) suggests an answer proffered by Alan Turing: that digital ICTs can process information on their own, in some sense just as humans and other biological life. ICTs can also communicate information to each other, without human intervention, but as linked processes designed by humans. We have evolved to the point where humans are not always “in the loop” of technology, but should be “on the loop” (Floridi 2014 , p. 30), designing and adapting the process. We perceive our world more and more in informational terms, and not primarily as physical entities (Floridi 2008 ). Increasingly, the educational world is dominated by information and our economies rest primarily on that asset. So our world is also blended, and it is blended so much that we hardly see the individual components of the blend any longer. Floridi ( 2014 ) argues that the world has become an “infosphere” (like biosphere) where we live as “inforgs.” What is real for us is shifting from the physical and unchangeable to those things with which we can interact.

Floridi also helps us to identify the next blend in education, involving ICTs, or specialized artificial intelligence (Floridi 2014 , 25; Norberg 2017 , 65). Learning analytics, adaptive learning, calibrated peer review, and automated essay scoring (Balfour 2013 ) are advanced processes that, provided they are good interfaces, can work well with the teacher— allowing him or her to concentrate on human attributes such as being caring, creative, and engaging in problem-solving. This can, of course, as with all technical advancements, be used to save resources and augment the role of the teacher. For instance, if artificial intelligence can be used to work along with teachers, allowing them more time for personal feedback and mentoring with students, then, we will have made a transformational breakthrough. The Edinburg University manifesto for teaching online says bravely, “Automation need not impoverish education – we welcome our robot colleagues” (Bayne et al. 2016 ). If used wisely, they will teach us more about ourselves, and about what is truly human in education. This emerging blend will also affect curricular and policy questions, such as the what? and what for? The new normal for education will be in perpetual flux. Floridi’s ( 2014 ) philosophy offers us tools to understand and be in control and not just sit by and watch what happens. In many respects, he has addressed the new normal for blended learning.

Literature of blended learning

A number of investigators have assembled a comprehensive agenda of transformative and innovative research issues for blended learning that have the potential to enhance effectiveness (Garrison and Kanuka 2004 ; Picciano 2009 ). Generally, research has found that BL results in improvement in student success and satisfaction, (Dziuban and Moskal 2011 ; Dziuban et al. 2011 ; Means et al. 2013 ) as well as an improvement in students’ sense of community (Rovai and Jordan 2004 ) when compared with face-to-face courses. Those who have been most successful at blended learning initiatives stress the importance of institutional support for course redesign and planning (Moskal et al. 2013 ; Dringus and Seagull 2015 ; Picciano 2009 ; Tynan et al. 2015 ). The evolving research questions found in the literature are long and demanding, with varied definitions of what constitutes “blended learning,” facilitating the need for continued and in-depth research on instructional models and support needed to maximize achievement and success (Dringus and Seagull 2015 ; Bloemer and Swan 2015 ).

Educational access

The lack of access to educational technologies and innovations (sometimes termed the digital divide) continues to be a challenge with novel educational technologies (Fairlie 2004 ; Jones et al. 2009 ). One of the promises of online technologies is that they can increase access to nontraditional and underserved students by bringing a host of educational resources and experiences to those who may have limited access to on-campus-only higher education. A 2010 U.S. report shows that students with low socioeconomic status are less likely to obtain higher levels of postsecondary education (Aud et al. 2010 ). However, the increasing availability of distance education has provided educational opportunities to millions (Lewis and Parsad 2008 ; Allen et al. 2016 ). Additionally, an emphasis on open educational resources (OER) in recent years has resulted in significant cost reductions without diminishing student performance outcomes (Robinson et al. 2014 ; Fischer et al. 2015 ; Hilton et al. 2016 ).

Unfortunately, the benefits of access may not be experienced evenly across demographic groups. A 2015 study found that Hispanic and Black STEM majors were significantly less likely to take online courses even when controlling for academic preparation, socioeconomic status (SES), citizenship, and English as a second language (ESL) status (Wladis et al. 2015 ). Also, questions have been raised about whether the additional access afforded by online technologies has actually resulted in improved outcomes for underserved populations. A distance education report in California found that all ethnic minorities (except Asian/Pacific Islanders) completed distance education courses at a lower rate than the ethnic majority (California Community Colleges Chancellor’s Office 2013 ). Shea and Bidjerano ( 2014 , 2016 ) found that African American community college students who took distance education courses completed degrees at significantly lower rates than those who did not take distance education courses. On the other hand, a study of success factors in K-12 online learning found that for ethnic minorities, only 1 out of 15 courses had significant gaps in student test scores (Liu and Cavanaugh 2011 ). More research needs to be conducted, examining access and success rates for different populations, when it comes to learning in different modalities, including fully online and blended learning environments.

Framing a treatment effect

Over the last decade, there have been at least five meta-analyses that have addressed the impact of blended learning environments and its relationship to learning effectiveness (Zhao et al. 2005 ; Sitzmann et al. 2006 ; Bernard et al. 2009 ; Means et al. 2010 , 2013 ; Bernard et al. 2014 ). Each of these studies has found small to moderate positive effect sizes in favor of blended learning when compared to fully online or traditional face-to-face environments. However, there are several considerations inherent in these studies that impact our understanding the generalizability of outcomes.

Dziuban and colleagues (Dziuban et al. 2015 ) analyzed the meta-analyses conducted by Means and her colleagues (Means et al. 2013 ; Means et al. 2010 ), concluding that their methods were impressive as evidenced by exhaustive study inclusion criteria and the use of scale-free effect size indices. The conclusion, in both papers, was that there was a modest difference in multiple outcome measures for courses featuring online modalities—in particular, blended courses. However, with blended learning especially, there are some concerns with these kinds of studies. First, the effect sizes are based on the linear hypothesis testing model with the underlying assumption that the treatment and the error terms are uncorrelated, indicating that there is nothing else going on in the blending that might confound the results. Although the blended learning articles (Means et al. 2010 ) were carefully vetted, the assumption of independence is tenuous at best so that these meta-analysis studies must be interpreted with extreme caution.

There is an additional concern with blended learning as well. Blends are not equivalent because of the manner on which they are configured. For instance, a careful reading of the sources used in the Means, et al. papers will identify, at minimum, the following blending techniques: laboratory assessments, online instruction, e-mail, class web sites, computer laboratories, mapping and scaffolding tools, computer clusters, interactive presentations and e-mail, handwriting capture, evidence-based practice, electronic portfolios, learning management systems, and virtual apparatuses. These are not equivalent ways in which to configure courses, and such nonequivalence constitutes the confounding we describe. We argue here that, in actuality, blended learning is a general construct in the form of a boundary object (Star and Griesemer 1989 ) rather than a treatment effect in the statistical sense. That is, an idea or concept that can support a community of practice, but is weakly defined fostering disagreement in the general group. Conversely, it is stronger in individual constituencies. For instance, content disciplines (i.e. education, rhetoric, optics, mathematics, and philosophy) formulate a more precise definition because of commonly embraced teaching and learning principles. Quite simply, the situation is more complicated than that, as Leonard Smith ( 2007 ) says after Tolstoy,

“All linear models resemble each other, each non nonlinear system is unique in its own way” (p. 33).

This by no means invalidates these studies, but effect size associated with blended learning should be interpreted with caution where the impact is evaluated within a particular learning context.

Study objectives

This study addressed student access by examining success and withdrawal rates in the blended learning courses by comparing them to face-to-face and online modalities over an extended time period at the University of Central Florida. Further, the investigators sought to assess the differences in those success and withdrawal rates with the minority status of students. Secondly, the investigators examined the student end-of-course ratings of blended learning and other modalities by attempting to develop robust if-then decision rules about what characteristics of classes and instructors lead students to assign an “excellent” value to their educational experience. Because of the high stakes nature of these student ratings toward faculty promotion, awards, and tenure, they act as a surrogate measure for instructional quality. Next, the investigators determined the conditional probabilities for students conforming to the identified rule cross-referenced by expected grade, the degree to which they desired to take the course, and course modality.

Student grades by course modality were recoded into a binary variable with C or higher assigned a value of 1, and remaining values a 0. This was a declassification process that sacrificed some specificity but compensated for confirmation bias associated with disparate departmental policies regarding grade assignment. At the measurement level this was an “on track to graduation index” for students. Withdrawal was similarly coded by the presence or absence of its occurrence. In each case, the percentage of students succeeding or withdrawing from blended, online or face-to-face courses was calculated by minority and non-minority status for the fall 2014 through fall 2015 semesters.

Next, a classification and regression tree (CART) analysis (Brieman et al. 1984 ) was performed on the student end-of-course evaluation protocol ( Appendix 1 ). The dependent measure was a binary variable indicating whether or not a student assigned an overall rating of excellent to his or her course experience. The independent measures in the study were: the remaining eight rating items on the protocol, college membership, and course level (lower undergraduate, upper undergraduate, and graduate). Decision trees are efficient procedures for achieving effective solutions in studies such as this because with missing values imputation may be avoided with procedures such as floating methods and the surrogate formation (Brieman et al. 1984 , Olshen et al. 1995 ). For example, a logistic regression method cannot efficiently handle all variables under consideration. There are 10 independent variables involved here; one variable has three levels, another has nine, and eight have five levels each. This means the logistic regression model must incorporate more than 50 dummy variables and an excessively large number of two-way interactions. However, the decision-tree method can perform this analysis very efficiently, permitting the investigator to consider higher order interactions. Even more importantly, decision trees represent appropriate methods in this situation because many of the variables are ordinally scaled. Although numerical values can be assigned to each category, those values are not unique. However, decision trees incorporate the ordinal component of the variables to obtain a solution. The rules derived from decision trees have an if-then structure that is readily understandable. The accuracy of these rules can be assessed with percentages of correct classification or odds-ratios that are easily understood. The procedure produces tree-like rule structures that predict outcomes.

The model-building procedure for predicting overall instructor rating

For this study, the investigators used the CART method (Brieman et al. 1984 ) executed with SPSS 23 (IBM Corp 2015 ). Because of its strong variance-sharing tendencies with the other variables, the dependent measure for the analysis was the rating on the item Overall Rating of the Instructor , with the previously mentioned indicator variables (college, course level, and the remaining 8 questions) on the instrument. Tree methods are recursive, and bisect data into subgroups called nodes or leaves. CART analysis bases itself on: data splitting, pruning, and homogeneous assessment.

Splitting the data into two (binary) subsets comprises the first stage of the process. CART continues to split the data until the frequencies in each subset are either very small or all observations in a subset belong to one category (e.g., all observations in a subset have the same rating). Usually the growing stage results in too many terminate nodes for the model to be useful. CART solves this problem using pruning methods that reduce the dimensionality of the system.

The final stage of the analysis involves assessing homogeneousness in growing and pruning the tree. One way to accomplish this is to compute the misclassification rates. For example, a rule that produces a .95 probability that an instructor will receive an excellent rating has an associated error of 5.0%.

Implications for using decision trees

Although decision-tree techniques are effective for analyzing datasets such as this, the reader should be aware of certain limitations. For example, since trees use ranks to analyze both ordinal and interval variables, information can be lost. However, the most serious weakness of decision tree analysis is that the results can be unstable because small initial variations can lead to substantially different solutions.

For this study model, these problems were addressed with the k-fold cross-validation process. Initially the dataset was partitioned randomly into 10 subsets with an approximately equal number of records in each subset. Each cohort is used as a test partition, and the remaining subsets are combined to complete the function. This produces 10 models that are all trained on different subsets of the original dataset and where each has been used as the test partition one time only.

Although computationally dense, CART was selected as the analysis model for a number of reasons— primarily because it provides easily interpretable rules that readers will be able evaluate in their particular contexts. Unlike many other multivariate procedures that are even more sensitive to initial estimates and require a good deal of statistical sophistication for interpretation, CART has an intuitive resonance with researcher consumers. The overriding objective of our choice of analysis methods was to facilitate readers’ concentration on our outcomes rather than having to rely on our interpretation of the results.

Institution-level evaluation: Success and withdrawal

The University of Central Florida (UCF) began a longitudinal impact study of their online and blended courses at the start of the distributed learning initiative in 1996. The collection of similar data across multiple semesters and academic years has allowed UCF to monitor trends, assess any issues that may arise, and provide continual support for both faculty and students across varying demographics. Table  1 illustrates the overall success rates in blended, online and face-to-face courses, while also reporting their variability across minority and non-minority demographics.

While success (A, B, or C grade) is not a direct reflection of learning outcomes, this overview does provide an institutional level indication of progress and possible issues of concern. BL has a slight advantage when looking at overall success and withdrawal rates. This varies by discipline and course, but generally UCF’s blended modality has evolved to be the best of both worlds, providing an opportunity for optimizing face-to-face instruction through the effective use of online components. These gains hold true across minority status. Reducing on-ground time also addresses issues that impact both students and faculty such as parking and time to reach class. In addition, UCF requires faculty to go through faculty development tailored to teaching in either blended or online modalities. This 8-week faculty development course is designed to model blended learning, encouraging faculty to redesign their course and not merely consider blended learning as a means to move face-to-face instructional modules online (Cobb et al. 2012 ; Lowe 2013 ).

Withdrawal (Table  2 ) from classes impedes students’ success and retention and can result in delayed time to degree, incurred excess credit hour fees, or lost scholarships and financial aid. Although grades are only a surrogate measure for learning, they are a strong predictor of college completion. Therefore, the impact of any new innovation on students’ grades should be a component of any evaluation. Once again, the blended modality is competitive and in some cases results in lower overall withdrawal rates than either fully online or face-to-face courses.

The students’ perceptions of their learning environments

Other potentially high-stakes indicators can be measured to determine the impact of an innovation such as blended learning on the academy. For instance, student satisfaction and attitudes can be measured through data collection protocols, including common student ratings, or student perception of instruction instruments. Given that those ratings often impact faculty evaluation, any negative reflection can derail the successful implementation and scaling of an innovation by disenfranchised instructors. In fact, early online and blended courses created a request by the UCF faculty senate to investigate their impact on faculty ratings as compared to face-to-face sections. The UCF Student Perception of Instruction form is released automatically online through the campus web portal near the end of each semester. Students receive a splash page with a link to each course’s form. Faculty receive a scripted email that they can send to students indicating the time period that the ratings form will be available. The forms close at the beginning of finals week. Faculty receive a summary of their results following the semester end.

The instrument used for this study was developed over a ten year period by the faculty senate of the University of Central Florida, recognizing the evolution of multiple course modalities including blended learning. The process involved input from several constituencies on campus (students, faculty, administrators, instructional designers, and others), in attempt to provide useful formative and summative instructional information to the university community. The final instrument was approved by resolution of the senate and, currently, is used across the university. Students’ rating of their classes and instructors comes with considerable controversy and disagreement with researchers aligning themselves on both sides of the issue. Recently, there have been a number of studies criticizing the process (Uttl et al. 2016 ; Boring et al. 2016 ; & Stark and Freishtat 2014 ). In spite of this discussion, a viable alternative has yet to emerge in higher education. So in the foreseeable future, the process is likely to continue. Therefore, with an implied faculty senate mandate this study was initiated by this team of researchers.

Prior to any analysis of the item responses collected in this campus-wide student sample, the psychometric quality (domain sampling) of the information yielded by the instrument was assessed. Initially, the reliability (internal consistency) was derived using coefficient alpha (Cronbach 1951 ). In addition, Guttman ( 1953 ) developed a theorem about item properties that leads to evidence about the quality of one’s data, demonstrating that as the domain sampling properties of items improve, the inverse of the correlation matrix among items will approach a diagonal. Subsequently, Kaiser and Rice ( 1974 ) developed the measure of sampling adequacy (MSA) that is a function of the Guttman Theorem. The index has an upper bound of one with Kaiser offering some decision rules for interpreting the value of MSA. If the value of the index is in the .80 to .99 range, the investigator has evidence of an excellent domain sample. Values in the .70s signal an acceptable result, and those in the .60s indicate data that are unacceptable. Customarily, the MSA has been used for data assessment prior to the application of any dimensionality assessments. Computation of the MSA value gave the investigators a benchmark for the construct validity of the items in this study. This procedure has been recommended by Dziuban and Shirkey ( 1974 ) prior to any latent dimension analysis and was used with the data obtained for this study. The MSA for the current instrument was .98 suggesting excellent domain sampling properties with an associated alpha reliability coefficient of .97 suggesting superior internal consistency. The psychometric properties of the instrument were excellent with both measures.

The online student ratings form presents an electronic data set each semester. These can be merged across time to create a larger data set of completed ratings for every course across each semester. In addition, captured data includes course identification variables including prefix, number, section and semester, department, college, faculty, and class size. The overall rating of effectiveness is used most heavily by departments and faculty in comparing across courses and modalities (Table  3 ).

The finally derived tree (decision rules) included only three variables—survey items that asked students to rate the instructor’s effectiveness at:

Helping students achieve course objectives,

Creating an environment that helps students learn, and

Communicating ideas and information.

None of the demographic variables associated with the courses contributed to the final model. The final rule specifies that if a student assigns an excellent rating to those three items, irrespective of their status on any other condition, the probability is .99 that an instructor will receive an overall rating of excellent. The converse is true as well. A poor rating on all three of those items will lead to a 99% chance of an instructor receiving an overall rating of poor.

Tables  4 , 5 and 6 present a demonstration of the robustness of the CART rule for variables on which it was not developed: expected course grade, desire to take the course and modality.

In each case, irrespective of the marginal probabilities, those students conforming to the rule have a virtually 100% chance of seeing the course as excellent. For instance, 27% of all students expecting to fail assigned an excellent rating to their courses, but when they conformed to the rule the percentage rose to 97%. The same finding is true when students were asked about their desire to take the course with those who strongly disagreed assigning excellent ratings to their courses 26% of the time. However, for those conforming to the rule, that category rose to 92%. When course modality is considered in the marginal sense, blended learning is rated as the preferred choice. However, from Table  6 we can observe that the rule equates student assessment of their learning experiences. If they conform to the rule, they will see excellence.

This study addressed increasingly important issues of student success, withdrawal and perception of the learning environment across multiple course modalities. Arguably these components form the crux of how we will make more effective decisions about how blended learning configures itself in the new normal. The results reported here indicate that blending maintains or increases access for most student cohorts and produces improved success rates for minority and non-minority students alike. In addition, when students express their beliefs about the effectiveness of their learning environments, blended learning enjoys the number one rank. However, upon more thorough analysis of key elements students view as important in their learning, external and demographic variables have minimal impact on those decisions. For example college (i.e. discipline) membership, course level or modality, expected grade or desire to take a particular course have little to do with their course ratings. The characteristics they view as important relate to clear establishment and progress toward course objectives, creating an effective learning environment and the instructors’ effective communication. If in their view those three elements of a course are satisfied they are virtually guaranteed to evaluate their educational experience as excellent irrespective of most other considerations. While end of course rating protocols are summative the three components have clear formative characteristics in that each one is directly related to effective pedagogy and is responsive to faculty development through units such as the faculty center for teaching and learning. We view these results as encouraging because they offer potential for improving the teaching and learning process in an educational environment that increases the pressure to become more responsive to contemporary student lifestyles.

Clearly, in this study we are dealing with complex adaptive systems that feature the emergent property. That is, their primary agents and their interactions comprise an environment that is more than the linear combination of their individual elements. Blending learning, by interacting with almost every aspect of higher education, provides opportunities and challenges that we are not able to fully anticipate.

This pedagogy alters many assumptions about the most effective way to support the educational environment. For instance, blending, like its counterpart active learning, is a personal and individual phenomenon experienced by students. Therefore, it should not be surprising that much of what we have called blended learning is, in reality, blended teaching that reflects pedagogical arrangements. Actually, the best we can do for assessing impact is to use surrogate measures such as success, grades, results of assessment protocols, and student testimony about their learning experiences. Whether or not such devices are valid indicators remains to be determined. We may be well served, however, by changing our mode of inquiry to blended teaching.

Additionally, as Norberg ( 2017 ) points out, blended learning is not new. The modality dates back, at least, to the medieval period when the technology of textbooks was introduced into the classroom where, traditionally, the professor read to the students from the only existing manuscript. Certainly, like modern technologies, books were disruptive because they altered the teaching and learning paradigm. Blended learning might be considered what Johnson describes as a slow hunch (2010). That is, an idea that evolved over a long period of time, achieving what Kaufmann ( 2000 ) describes as the adjacent possible – a realistic next step occurring in many iterations.

The search for a definition for blended learning has been productive, challenging, and, at times, daunting. The definitional continuum is constrained by Oliver and Trigwell ( 2005 ) castigation of the concept for its imprecise vagueness to Sharpe et al.’s ( 2006 ) notion that its definitional latitude enhances contextual relevance. Both extremes alter boundaries such as time, place, presence, learning hierarchies, and space. The disagreement leads us to conclude that Lakoff’s ( 2012 ) idealized cognitive models i.e. arbitrarily derived concepts (of which blended learning might be one) are necessary if we are to function effectively. However, the strong possibility exists that blended learning, like quality, is observer dependent and may not exist outside of our perceptions of the concept. This, of course, circles back to the problem of assuming that blending is a treatment effect for point hypothesis testing and meta-analysis.

Ultimately, in this article, we have tried to consider theoretical concepts and empirical findings about blended learning and their relationship to the new normal as it evolves. Unfortunately, like unresolved chaotic solutions, we cannot be sure that there is an attractor or that it will be the new normal. That being said, it seems clear that blended learning is the harbinger of substantial change in higher education and will become equally impactful in K-12 schooling and industrial training. Blended learning, because of its flexibility, allows us to maximize many positive education functions. If Floridi ( 2014 ) is correct and we are about to live in an environment where we are on the communication loop rather than in it, our educational future is about to change. However, if our results are correct and not over fit to the University of Central Florida and our theoretical speculations have some validity, the future of blended learning should encourage us about the coming changes.

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The authors acknowledge the contributions of several investigators and course developers from the Center for Distributed Learning at the University of Central Florida, the McKay School of Education at Brigham Young University, and Scholars at Umea University, Sweden. These professionals contributed theoretical and practical ideas to this research project and carefully reviewed earlier versions of this manuscript. The Authors gratefully acknowledge their support and assistance.

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How Technology Is Changing the Future of Higher Education

Labs test artificial intelligence, virtual reality and other innovations that could improve learning and lower costs for Generation Z and beyond.

By Jon Marcus

This article is part of our latest Learning special report . We’re focusing on Generation Z, which is facing challenges from changing curriculums and new technology to financial aid gaps and homelessness.

MANCHESTER, N.H. — Cruising to class in her driverless car, a student crams from notes projected on the inside of the windshield while she gestures with her hands to shape a 3-D holographic model of her architecture project.

It looks like science fiction, an impression reinforced by the fact that it is being demonstrated in virtual reality in an ultramodern space with overstuffed pillows for seats. But this scenario is based on technology already in development.

The setting is the Sandbox ColLABorative, the innovation arm of Southern New Hampshire University, on the fifth floor of a downtown building with panoramic views of the sprawling red brick mills that date from this city’s 19th-century industrial heyday.

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Theirs is not a future of falling enrollment, financial challenges and closing campuses. It’s a brighter world in which students subscribe to rather than enroll in college, learn languages in virtual reality foreign streetscapes with avatars for conversation partners, have their questions answered day or night by A.I. teaching assistants and control their own digital transcripts that record every life achievement.

The possibilities for advances such as these are vast. The structure of higher education as it is still largely practiced in America is as old as those Manchester mills, based on a calendar that dates from a time when students had to go home to help with the harvest, and divided into academic disciplines on physical campuses for 18- to 24-year-olds.

Universities may be at the cutting edge of research into almost every other field, said Gordon Jones, founding dean of the Boise State University College of Innovation and Design. But when it comes to reconsidering the structure of their own, he said, “they’ve been very risk-averse.”

Now, however, squeezed by the demands of employers and students — especially the up and coming Generation Z — and the need to attract new customers, some schools, such as Boise State and Southern New Hampshire University, are starting labs to come up with improvements to help people learn more effectively, match their skills with jobs and lower their costs.

More than 200 have added senior executives whose titles include the words “digital” or “innovation,” the consulting firm Entangled Solutions found; many were recruited from the corporate and tech sectors. M.I.T. has set up a multimillion-dollar fund to pay for faculty to experiment with teaching innovations .

Some colleges and universities are collaborating on such ideas in groups including the University Innovation Alliance and the Marvel Universe-worthy HAIL Storm — it stands for Harvesting Academic Innovation for Learners — a coalition of academic innovation labs.

If history is a guide, the flashiest notions being developed in workshops in these places won’t get far. University campuses are like archaeological digs of innovations that didn’t fulfill their promises. Even though the biggest leap forward of the last few decades, for example — delivering courses online — appears to have lowered costs , the graduation rates of online higher education remain much lower than those of programs taught in person .

“One of the most important things we do here is disprove and dismantle ideas,” said William Zemp, chief strategy and innovation officer at Southern New Hampshire University.

“There’s so much white noise out there, you have to be sort of a myth buster.”

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College by Subscription

One of these would transform the way students pay for higher education. Instead of enrolling, for example, they might subscribe to college; for a monthly fee, they could take whatever courses they want, when they want, with long-term access to advising and career help.

The Georgia Institute of Technology is one of the places mulling a subscription model, said Richard DeMillo, director of its Center for 21st Century Universities. It would include access to a worldwide network of mentors and advisers and “whatever someone needs to do to improve their professional situation or acquire a new skill or get feedback on how things are going.”

Boise State is already piloting this concept. Its Passport to Education costs $425 a month for six credit hours or $525 for nine in either of two online bachelor’s degree programs. That’s 30 percent cheaper than the in-state, in-person tuition.

Paying by the month encourages students to move faster through their educations, and most are projected to graduate in 18 months, Mr. Jones said. The subscription model has attracted 47 students so far, he said, with another 94 in the application process.

However they pay for it, future students could find other drastic changes in the way their educations are delivered.

Your Teacher Is a Robot

Georgia Tech has been experimenting with a virtual teaching assistant named Jill Watson, built on the Jeopardy-winning IBM Watson supercomputer platform. This A.I. answers questions in a discussion forum alongside human teaching assistants; students often can’t distinguish among them, their professor says. More Jill Watsons could help students get over hurdles they encounter in large or online courses. The university is working next on developing virtual tutors, which it says could be viable in two to five years .

S.N.H.U., in a collaboration with the education company Pearson, is testing A.I. grading. Barnes & Noble Education already has an A.I. writing tool called bartleby write , named for the clerk in the Herman Melville short story, that corrects grammar, punctuation and spelling, searches for plagiarism and helps create citations.

At Arizona State University, A.I. is being used to watch for signs that A.S.U. Online students might be struggling, and to alert their academic advisers.

“If we could catch early signals, we could go to them much earlier and say, ‘Hey you’re still in the window’ ” to pass, said Donna Kidwell, chief technology officer of the university’s digital teaching and learning lab, EdPlus.

Another harbinger of things to come sits on a hillside near the Hudson River in upstate New York, where an immersion lab with 15-foot walls and a 360-degree projection system transports Rensselaer Polytechnic Institute language students to China , virtually.

The students learn Mandarin Chinese by conversing with A.I. avatars that can recognize not only what they say but their gestures and expressions, all against a computer-generated backdrop of Chinese street markets, restaurants and other scenes.

Julian Wong, a mechanical engineering major in the first group of students to go through the program, “thought it would be cheesy.” In fact, he said, “It’s definitely more engaging, because you’re actively involved with what’s going on.”

Students in the immersion lab mastered Mandarin about twice as fast as their counterparts in conventional classrooms, said Shirley Ann Jackson, the president of Rensselaer.

Dr. Jackson, a physicist, was not surprised. The students enrolling in college now “grew up in a digital environment,” she said. “Why not use that to actually engage them?”

Slightly less sophisticated simulations are being used in schools of education, where trainee teachers practice coping with simulated schoolchildren. Engineering students at the University of Michigan use an augmented-reality track to test autonomous vehicles in simulated traffic.

A Transcript for Life

The way these kinds of learning get documented is also about to change. A race is underway to create a lifelong transcript.

Most academic transcripts omit work or military histories, internships, apprenticeships and other relevant experience. And course names such as Biology 301 or Business 102 reveal little about what students have actually learned.

“The learner, the learning provider and the employer all are speaking different languages that don’t interconnect,” said Michelle Weise, chief innovation officer at the Strada Institute for the Future of Work.

A proposed solution: the “interoperable learning record,” or I.L.R. (proof that, even in the future, higher education will be rife with acronyms and jargon).

The I.L.R. would list the specific skills that people have learned — customer service, say, or project management — as opposed to which courses they passed and majors they declared. And it would include other life experiences they accumulated.

This “digital trail” would remain in the learner’s control to share with prospective employers and make it easier for a student to transfer academic credits earned at one institution to another.

American universities, colleges and work force training programs are now awarding at least 738,428 unique credentials , according to a September analysis by a nonprofit organization called Credential Engine, which has taken on the task of translating these into a standardized registry of skills.

Unlike transcripts, I.L.R.s could work in two directions. Not only could prospective employees use them to look for jobs requiring the skills they have; employers could comb through them to find prospective hires with the skills they need.

“We’re trying to live inside this whole preindustrial design and figure out how we interface with technology to take it further,” said Dr. Kidwell of Arizona State. “Everybody is wrangling with trying to figure out which of these experiments are really going to work.”

This story was produced in collaboration with The Hechinger Report , a nonprofit, independent news organization focused on inequality and innovation in education.

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Using technology in special education: current practices and trends

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• Oluwabunmi Adewoyin Olakanmi   ORCID: orcid.org/0000-0001-6281-0285 1 ,
• Gokce Akcayir 1 ,
• Oluwbukola Mayowa Ishola 2 &
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Recent reports suggest an increase in the number of individuals with cognitive and developmental disabilities. To ensure equal opportunities for these learners, special education practices must be appropriately improved and scaled. Educational and assistive technologies are a possible avenue for meeting this need. To provide insight into recent technology practices in special education contexts, this study reviews recent literature (2014—2018) on the use of technology to support learners with cognitive and developmental disabilities. This review included 126 publications, which were a combination of journal articles and conference papers, found through the ACM, IEEE, ScienceDirect, and SSCI databases. The publications were analyzed to determine their general characteristics (e.g., number and age of participants and lengths of interventions), the contexts of technology use, specific learner characteristics, the subjects or skills the intervention(s) aimed to improve, the characteristics of the technologies, and the outcomes associated with their use. The results revealed that the most examined technology was games and the most studied outcome was improvements to learners’ cognitive skills. Additionally, participants in the majority of the publications were pre-college students with learning disabilities. The most studied topics belonged to the natural sciences while job skills were one of the least studied. Interventions were primarily conducted in formal educational environments and were implemented over 5–10 weeks in most cases. Based on the review, more detail in reporting and more attention to promoting life, job, and social skills are recommended.

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How Has Technology Changed Education?

Technology has impacted almost every aspect of life today, and education is no exception. Or is it? In some ways, education seems much the same as it has been for many years. A 14th century illustration by Laurentius de Voltolina depicts a university lecture in medieval Italy. The scene is easily recognizable because of its parallels to the modern day. The teacher lectures from a podium at the front of the room while the students sit in rows and listen. Some of the students have books open in front of them and appear to be following along. A few look bored. Some are talking to their neighbors. One appears to be sleeping. Classrooms today do not look much different, though you might find modern students looking at their laptops, tablets, or smart phones instead of books (though probably open to Facebook). A cynic would say that technology has done nothing to change education.

However, in many ways, technology has profoundly changed education. For one, technology has greatly expanded access to education. In medieval times, books were rare and only an elite few had access to educational opportunities. Individuals had to travel to centers of learning to get an education. Today, massive amounts of information (books, audio, images, videos) are available at one’s fingertips through the Internet, and opportunities for formal learning are available online worldwide through the Khan Academy, MOOCs, podcasts, traditional online degree programs, and more. Access to learning opportunities today is unprecedented in scope thanks to technology.

Opportunities for communication and collaboration have also been expanded by technology. Traditionally, classrooms have been relatively isolated, and collaboration has been limited to other students in the same classroom or building. Today, technology enables forms of communication and collaboration undreamt of in the past. Students in a classroom in the rural U.S., for example, can learn about the Arctic by following the expedition of a team of scientists in the region, read scientists’ blog posting, view photos, e-mail questions to the scientists, and even talk live with the scientists via a videoconference. Students can share what they are learning with students in other classrooms in other states who are tracking the same expedition. Students can collaborate on group projects using technology-based tools such as wikis and Google docs. The walls of the classrooms are no longer a barrier as technology enables new ways of learning, communicating, and working collaboratively.

Technology has also begun to change the roles of teachers and learners. In the traditional classroom, such as what we see depicted in de Voltolina’s illustration, the teacher is the primary source of information, and the learners passively receive it. This model of the teacher as the “sage on the stage” has been in education for a long time, and it is still very much in evidence today. However, because of the access to information and educational opportunity that technology has enabled, in many classrooms today we see the teacher’s role shifting to the “guide on the side” as students take more responsibility for their own learning using technology to gather relevant information. Schools and universities across the country are beginning to redesign learning spaces to enable this new model of education, foster more interaction and small group work, and use technology as an enabler.

Technology is a powerful tool that can support and transform education in many ways, from making it easier for teachers to create instructional materials to enabling new ways for people to learn and work together. With the worldwide reach of the Internet and the ubiquity of smart devices that can connect to it, a new age of anytime anywhere education is dawning. It will be up to instructional designers and educational technologies to make the most of the opportunities provided by technology to change education so that effective and efficient education is available to everyone everywhere.

You can help shape the influence of technology in education with an Online Master of Science in Education in Learning Design and Technology from Purdue University Online. This accredited program offers studies in exciting new technologies that are shaping education and offers students the opportunity to take part in the future of innovation.

Learn more about the online MSEd in Learning Design and Technology at Purdue University today and help redefine the way in which individuals learn. Call (877) 497-5851 to speak with an admissions advisor or to request more information.

UT Academic Affairs Presents at Innovating Teaching and Learning in the Era of Generative AI Conference

Four members of UT Austin’s Academic Affairs community presented at a recent The University of Texas System conference  hosted by UT San Antonio’s Office of Academic Innovation . Strategic Academic Initiatives postdoctoral fellows Natalie Murray, PhD, and Raj Sankaranarayanan, PhD, Kory Bieg, associate professor and associate dean of Academic Affairs in the School of Architecture, and Julie Schell, EdD, assistant vice provost and director of the Office of Academic Technology, all shared their expertise on integrating generative AI into higher education.

The conference,  Innovating Teaching and Learning in the Era of Generative AI ,  brought together educators from across UT System to explore how generative AI and other emerging technologies are transforming teaching and learning. It was held in-person March 6-8.

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How technology is shaping education in Saudi Arabia

During the pandemic, an estimated 1.500 million students missed school, institutions adopted smart technologies to ensure the continuity of education. This wave of digital transformation brings long-term benefits and goes beyond the mere growth of distance learning.

The importance of education in supporting the success of Saudi Vision 2030 cannot be overstated.  The transformation of the economy through discovery and innovation across a multitude of sectors ranging from digital services to life sciences and clean sustainable energy requires a well-educated workforce. 

“Saudi Arabia clearly understands this and has led the way in establishing top-notch institutions of advanced research and higher learning.  From what I see across the nation, there is an emphasis on STEM. It is recognized that these areas are the cornerstones of economic transformation. The organization that I work for King Abdullah University of Science and Technology (KAUST) is an example of those efforts,’ explained Jason Ross, CIO at KAUST. “There was a time when this region, and the greater Islamic world, served as the epicentre of global scientific advancement.  The famous “House of Wisdom” or Bayt Al-Hikma served as a beacon of knowledge while much of the western world descended into the depths of the Dark Ages a millennium ago.  So, the pursuit of scientific discovery and technological innovation is just part of the culture, and the reinvigoration of those educational pursuits will serve as the foundation of the Kingdom’s transformation success for decades to come.”

Thus, the digital transformation of the education sector is another important part of Vision 2030’s success, to ensure young people in the Kingdom have the right skills for the future world of work. According to PwC, Hopes and Fear survey, 57% of the respondents in Saudi Arabia are either excited or confident about the future of their children and that technology will offer a world full of possibilities for their kids.

“Educational innovation must recognize that the digital landscape has opened up so many opportunities to provide education differently.  The skills of tomorrow require agility and on-demand delivery that cannot easily fit into the traditional educational journey.  There has been an over-emphasis on ‘credentialing’ that has made higher education too expensive and in some cases irrelevant to the needs of the digital workforce. The younger generations of digital natives know how to find the right answer and learn the skills they want to acquire through an ever-expanding array of social media channels.”

“I can’t speak for the entire country but I know that the requirement to adopt a distance learning delivery model at the beginning of the pandemic for all levels of education ranging from K-12 to university studies forced a rapid deployment of technological solutions.  This shift instigated an urgent effort to train everyone on the proper use of those tools to keep students on track in their educational journey,” added Ross.

As KAUST’s CIO Ross explains he was fortunate to have already in place a comprehensive portfolio of digital capabilities that allowed us to serve our multinational workforce.  However, KAUST’s main challenge early on during the pandemic was identifying individuals who had not used some of those tools because they were only optional, but when remote working required the usage of those solutions the IT department had to respond quickly with a training plan and related documentation to get everyone up-to-speed and productive as soon as possible.  

KAUST has launched numerous initiatives over the last couple of years in recognition of the ever-changing nature of the digital landscape. One of the key initiatives that Ross had the privilege of launching is a program that focuses on pushing the boundaries of digital innovation by creating a truly unique and immersive “Smart City” experience within the KAUST community. This effort seeks to combine existing technologies with emerging capabilities that are being developed by our researchers and students to curate experiences that can be piloted in our community, demonstrating that innovation is fundamental to the KAUST ethos.  

“KAUST Smart program is a cross-functional initiative that engages the entire KAUST organization and community through a dedicated team that works with various KAUST departments to identify opportunities for enhanced digital experiences and drives those efforts to achieve meaningful outcomes. KAUST Smart partners with companies and organizations to develop, test, and pilot technologies and to take advantage of our unique city environment. We do this through design-thinking workshops, co-development hackathons, prototype construction, experimentations, and eventually the deployment of technologies and solutions in a real-world operational setting throughout various parts of the KAUST community.”

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Use of Digital Educational Technologies among Nursing Students and Teachers: An Exploratory Study

Fernanda loureiro.

1 Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Escola Superior de Saúde Egas Moniz, 2829-551 Almada, Portugal; moc.liamg@me2assenav

Luís Sousa

2 Comprehensive Health Research Centre (CHRC), Nursing Department, Universidade de Évora, 7004-516 Évora, Portugal; tp.aroveu@smml

Vanessa Antunes

The emergence of digital educational technologies (DET) raises questions regarding the personalization of both teaching and care. DET use implies profound changes with consequences in nursing care and in nursing teaching-learning process. With the purpose of contributing to the improvement of the teaching-learning process through the use of DET, an exploratory-descriptive, cross-sectional, and observational study, with a quantitative approach (descriptive and inferential statistics), was developed. Online questionnaires were applied ( n = 140 students and n = 23 teachers) after ethics committee approval. Results point to low cost and access without time/space limits as the main benefits, and decreased interaction, less physical contact, and technical difficulties as constraints. Globally, there was no difference between students and teachers in the use of DET. Still, men report more constraints than women. In this sample, the use of DET is still at an early stage. Both students and teachers are still unfamiliar with the scope and possibilities of these tools, not taking full advantage of the potential they have to offer. The impact of DET used in personalized nursing care is still yet to be understood.

1. Introduction

Digital educational technologies (DET) have been used increasingly in recent years due to technological evolution. It implies profound changes in teaching practices with consequences in the teaching-learning process [ 1 ]. The use of platforms not only for providing academic content, but also for distance learning, have a great impact on both teachers and students: in the format of the contents that are made available, in the language used, class duration, time management, methods and hours of study, and in communication strategies. The recent public health pandemic emphasized the need for digital technologies use to maintain activity with social distance. In this context, DET represents an added value since they increase the interactivity and promote additional space for knowledge construction [ 2 ]. DET can be used in an asynchronous or a synchronous way. When used in its asynchronous form, it offers as added benefits: the possibility of a flexible schedule, the availability of quality material at low cost, and the potential to reach a high number of students without geographical limits [ 3 ]. However, issues on humanization of education and knowledge systematization are raised and, additionally, although DETs offer a wide range of possibilities, they do not guarantee learning quality [ 4 ]. Still, there is some reluctance in its use, particularly by teachers, as DETs break with traditional education, placing the emphasis on the student as the protagonist of the teaching-learning process [ 5 ].

As technology evolves, it has been integrated into different areas where education is included. Having this in mind, DET can be defined as the use of technological resources to improve teaching and learning, promote educational development and access to data and material [ 1 ]. It implies the use of virtual scenarios, online platforms, and digital resources, among others. Historically, educational sciences have used DET as a resource to improve teaching since the 80s, but in nursing its use is more recent. Benefits can be pointed out from its use, such as improved interaction and content integration [ 2 , 4 ], easy access, more dynamism and interaction with students outside a physical school space [ 1 ]. However, constrains can also be pointed to, namely financial (adopting state-of-the-art technology can be expensive) and personal (teachers may be reticent to change traditional educational methods). It is not integrated in any theory of learning, but it fits a diverse theoretical framework. A major element of this theoretical frameworks is socio-constructivism that underlines the role of social processes in individuals’ learning [ 6 ]. Social constructivism-based theory understands learning as a process that occurs through supportive collaboration with other people and leads to knowledge and evolution [ 7 ].

In nursing, the use of DET is seen as a resource that is complementary to traditional teaching [ 4 ] with increasing use and good results such as enhanced nursing students’ problem-solving skills [ 6 ]. An example of the use of DET with other educational strategies is its use combined with case studies improving its benefits [ 8 ]. However, Chavaglia et al. [ 9 ] pointed to the need for greater diversification in the use of DET. In their study with undergraduate nursing students, they concluded that digital power point presentations, email and a google search engine were the predominant digital tools used by students.

A link between the use of DET, nursing education and sustainability is also found in the literature. Education in sustainable development is recognized as important particularly in health professions such as nursing. The use of DET offers an opportunity to reinforce this link since DET are sustainable and can be applied with low environmental impact [ 10 ]. Other innovative andragogical approaches are also being used, such as augmented reality and virtual simulation. Foronda et al. state that results from the use of these technologies are encouraging, since they suggest efficacy in improving nursing students learning outcomes [ 11 ].

However, some caution is required in the implementation of DET, especially by teachers. They have to be conscious of factors that influence the success in DET’s application, and a minimum quality requirement has to be assured [ 10 ]. Usually, the types of DETs used by teachers are limited to those available in their institution and with easy access [ 12 ], which limits the quality of teaching and consequently the quality of the learning process. On the other hand, there has been a greater trend towards personalization of care and patient-centeredness. The use of this type of technology in teaching and its impact on care is still unknown. However, it seems to be consensual that DET may expand nursing education and consequently improve patient outcomes [ 11 ].

For the present study non-presential classroom was considered as classes that occur without physical presence in the same room of both students and teachers. The purpose of this study is to contribute to the improvement of the teaching-learning process in the nursing undergraduate degree through the use of DET in non-presential classroom teaching. The objective is to evaluate the use of DET in non-presential classroom teaching, benefits, constraints, and implications for teaching-learning process in nursing students. The following questions were defined: how is the use DET fulfilled in non-presential classroom teaching in nursing? What are the benefits and constrains to the use of DET? What are the implications for teaching-learning process of nursing students with the use of DET?

2. Materials and Methods

2.1. study design.

An exploratory-descriptive, cross-sectional, and observational study was outlined. The research design fits into the quantitative paradigm with the use of surveys as a data collection technique.

The study was approved by the school board as well as by the school ethics committee. It was an independent study not integrated in any course or curricular unit. The study was applied to 230 inquiries and invitation to participate in the study was sent by email with a survey link attached. Prior to fill the survey information regarding framework, aim, confidentiality, ethical issues and researchers contacts was provided. Consent was obtained prior to data collection. Participants had to mandatorily give their consent to advance with participation.

2.2. Sample and Setting

The population consists of all nursing students and teachers who lecture the undergraduate nursing course at a private nursing school in Lisbon, Portugal. The sample is therefore of simple non-probabilistic type. Students from all academic years were invited to participate ( n = 205). Teachers who teach classes of any type ( n = 25) were selected and teachers involved solely in clinical nursing were excluded.

2.3. Instrument

The online survey designed for this study includes 60 items based on previously published literature [ 13 , 14 , 15 , 16 ]. It had two parts: first part with sociodemographic data such as sex, age, and type of experience in the use of DET. Additional data collected included academic year for students’ and professional experience and type of employment for teachers. The second part of the survey had questions related to the use of DET, selected and adapted from a previous literature review.

Types of DET [ 13 ] included it a 5-point Likert scale with 23 items within 5 dimensions (organizational tools, communications tools, presentation tools, learning assessment tools and identity transformation tools). Benefits of using DET include 10 dichotomous items and were retrieved from Kokol et al. [ 14 ]. These authors applied a survey to 125 nursing students’ study and identified benefits and challenges of online education. Constraints of using DET was adapted from Lloyd et al. [ 15 ] and comprise 13 dichotomous items. The original scale included 22 items and authors performed exploratory factor analysis for validity and Cronbach alpha for reliability. Four factors were extracted that had high reliabilities (0.892; 0.806; 0.805; 0.870). In our study, we adapted the items for our context. Finally, implications in the use of DET include 14 dichotomous items adapted from an integrative review on digital technologies in the teaching of nursing skills by Silveira and Cogo [ 16 ].

Before its application, the surveys were sent to 5 experts. Content validity index was calculated as described by Polit and Becket [ 17 ] to verify the extend of expert agreement. A value of 0.90 was obtained which is considered adequate by the same authors.

Additionally, for instrument validity, to understand how types of DET were related and to favor statistical analysis, exploratory principal components factor analysis with varimax rotation was performed on the 23 items that addressed types of DET. The Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO = 0.759. Additionally, Bartlett’s sphericity test indicated that correlations between items were sufficiently large, X 2 = 1146.772, p < 0.001.

Initially, for the first-dimension sevens factors were identified that explained 64.9% of variance. However, two item (calendars and electronic portfolio) had correlations <0.3. By removing them and forcing analysis to the initial 5 factors that were identified in the literature, a 53.8% of the variance was obtained as explained on Table 1 . Regarding communalities values ≥0.4 were considered. We also included the item: “Instant messages” although h 2 value is 0.386 because it is a marginal value and due to its conceptual importance in the context of this study.

Exploratory factorial analysis for types of DET.

*-h 2 : communalities.

For reliability assessment the Cronbach Alpha coefficient as well as the Kuder-Richardson formula were determined. Regarding types of DET, the alpha Cronbach values range between 0.594 in factor 5 and 0.739 in factor 1 as shown in Table 1 . For benefits, constraints and implications thar are dichotomous items in the Kuder-Richardson formula indicated the following values: 0.670 (benefits); 0.502 (constrains) and 0.584 (implications). The values obtained are considered satisfactory [ 18 ], and additionally, it must be taken into consideration that it is a first attempt to use this instrument.

2.4. Procedure

Participant’s recruitment was performed by email. Since this was an independent study, not integrated in any course or discipline, researchers contacted students and professors through email addresses. An email was sent to all the participants inviting them to participate in the study. This email contained a survey link that allowed access to the online instrument. It was available for 1 week, and after that time, a reminder was sent, and the survey was available for one week more. The number of answered surveys were monitored by the researchers at the end of each week. After two weeks had passed the link was removed and data was transferred for analysis. All surveys were fully completed by participants.

2.5. Data Analysis

Descriptive and inferential statistics were used for data analysis. Statistical analyses was performed with SPSS statistical package, 27.0 version (SPSS, Inc., Chicago, IL, USA). Categorical variables are presented as percentages. All variables followed a non-normal distribution so nonparametric test namely Mann-Whitney U test was used for comparisons between groups. To verify association between categorical variables Phi/Cramer V was used. A p value of 0.05 or less was considered statistically significant.

3.1. Sample Characteristics

Of the study population, 163 subjects (140 students and 23 teachers) completed and returned the surveys, so the response rate was 70.8%. Most respondents were female (85%; n = 138) and had no training or formal education on DET, only experience as users (86%; n = 140). Sociodemographic characteristics are summarized in Table 2 .

Sample sociodemographic characteristics.

3.2. Types of DET

Regarding the type of DET used, they were grouped into the five categories identified in factorial analysis. For presentation data, answers were clustered into two group: very frequently and frequently answers and occasionally, rarely or never answers. Results are summarized in Table 3 .

Results regarding type of DET used by teachers and students.

VF—very frequently; F—frequently; O—occasionally; R—rarely; N—never; * significant at p < 0.05.

Mann-Whitney U test was used to assess if there were association between student’s sample and teachers sample and the use of DET. No significative association was found except on the item “tests” (U = 2094.5; p = 0.016).

As to technologies used, results show the use of the technologies provided and available in the institution where the survey was applied: Moodle (98.8%; n = 161) and Microsoft Teams (98.2%; n = 160). Other technologies identified included Zoom (22.1%; n = 36), Mentimeter (4.9%; n = 8), Kahoot (4.3%; n = 7), Skype (2.5%; n = 4) and Google classrooms (1.2%; n = 2).

As to type of classes DET they were mostly used in theoretical class (93%; n = 153). In practical and laboratory classes DET were used less frequently (37% and 28%, respectively).

3.3. Benefits and Constrains in the Use of DET

Regarding benefits and constraints responses are summarized in Table 4 .

Results regarding benefits and constraints in the use of DET.

* significant at p < 0.05.

To assess association between type of inquiries (students or teachers) Phi/Cramer’s V was calculated. There were only significant statistical differences between teachers and students in two items: “it allows the integrating of multiple learning tools (pdf, links, app, among others)” and “promotes student accountability”. Teachers refer to more benefits in the integration of multiple learning tools (V = 0.170; p = 0.030), and students attribute more benefits in item: “promotes accountability” (V = 0.170; p = 0.030). Regarding constrains, the same procedure was followed; however, no significant statistical differences were found.

3.4. Implications in the Use of DET

The last question of the questionnaire referred to implications in the use of DET. Answers are synthetized in Table 5 .

Results regarding implications in the use of DET.

Regarding implications also Phi/Cramer’s V was calculated to assess association between type of inquiries (students or teachers) and implications. Nevertheless, there were no significant statistical differences between the groups.

Globally, there was no difference between students and teachers in the use of DET. Still, when analysis is performed by gender differences were found regarding Factor 3 in types of DET and constrains.

Women got a higher score concerning the types of DET used (Factor 3) compared to men, that is, women use these resources more than men (U = 1531.5; p = 0.040). Still, men report more constraints than women in the use of DET (U = 2255; p = 0.005).

As stated earlier, all questions had an open space for additional answers; however, there were few answers and those who did respond mentioned aspects already identified.

4. Discussion

This study allowed us to gather evidence related to the use of DET, being a first step to the improvement of the teaching-learning processes in the nursing undergraduate degree. Our results show a scarce use of DET by both teachers and students. Literature points to an increase in the use of DET in nursing teaching [ 16 ]. Our results show that there is space for improvement in this area. A possible explanation for this result may be the existence of interpersonal and institutional barriers, training and technological constrains and the lack of cost/benefit analysis [ 15 ].

Overall, 86% of our sample had no formal training on DET, which is in line with the literature. However, studies highlight that digital technologies are used both with academic and personal purposes [ 19 ]. Probably teachers in this sample do not feel the need for formal education as the day-to-day use of these tools allows them to use them without limitations or constraints. Additionally, digital tools are, naturally intuitive and user friendly. When considering our sample of students, they belong to a generation raised with information technology, internet, and social networks, which make them more confident manipulating new platforms and devices, not really requiring training in the area.

As mentioned in the results chapter, the statistical procedure allowed to group the types of DET in five new factors, categorized as follows: Training and Discussion Tools (factor 1); Communication tools (factor 2); Presentation and assessment tools (factor 3); Organization tools (factor 4); and Complementary learning tools (factor 5).

The training and discussion tools (factor 1) included the following items: discussion forums, blogs, avatars, virtual scenarios, and immersive technology. In this category, results show that avatars and virtual scenarios are poorly used. This types of DET are identified as particularly relevant in nursing education since they may provide a solution to issues such as: faculty shortages, scarcity of clinical placements and limited onsite laboratory space [ 11 ]. Additionally, its use allows students to repeatedly simulate procedures and care or even recreate high risk events that they may not contact within clinical context [ 11 , 16 ].

Communication tools category (factor 2) includes: videocalls, web conferences, audio, and video. Videocalls (74%) and audio (74%) are the preferred communication tools for the total sample. Although web conferences can save students time and prevent the inconveniences of traveling, video and audio conferencing provide the benefit of visual aids, allowing participants to make use of multiple senses, improving their concentration levels and increasing their capacity to absorb more information [ 20 , 21 ].

Within the presentation and assessment tools category (factor 3), the following items were identified: slides, tutorials, image sharing, bibliography sharing, activities, and tests. Tutorials got the lowest score (39%). This technology is still underused in our sample, but perhaps the changes in the lifestyle of today’s society could lead to a greater need and recognition of its use in this setting. The traditional classroom can restrict daily life, as it requires anticipated planning. Otherwise, the tutorial can be paused, rewound or fast-forwarded, and the lecture can be heard as many times as needed. These characteristics fit the current generation of students that crave the digital world and are extreme consumers of technology [ 22 ]. On the other hand, slides got the most representative score, as it is used by 89% of our sample. The use of DET to share slides can be framed as a simplistic and traditional use of this type of technologies.

In this category, it is also possible to verify an association between gender and the use of presentation and assessment tools, as women got a higher score compared to men (U = 1531.5; p = 0.040). Additionally, men report more constraints than women in the use of DET (U = 2255; p = 0.005). These results are quite interesting, as there is the general idea that women are outnumbered in informatics. Nevertheless, literature shows that the very first computer programmers and IT users were women, and that technology are more frequently used in female-dominated areas [ 23 ], such as teaching and nursing. Cai et al. [ 24 ] found that there are no significant gender differences in the attitudes toward technology. However, these authors argue that in the academic context, women are more prepared for technology use than the general female population.

In types of DET, factor 4 covers the following organization tools: online schedule, mind maps or graphic organizers, and file and management store. This last one is the most used by 67% of our sample. It is a more cost-effective system, as it reduces organization bureaucracy and saves space. Students can easily search, access, and share files. Additionally, support for decision-making and knowledge discovery can be achieved in an effectively way through the use of massive amounts of data that can be easily stored [ 24 ].

Instant messages and chats, research, and social network were considered as complementary learning tools (factor 5). Instant messages and chats (84%) are the mostly used by both students and teachers in our sample. They may be more relevant because they are one of the oldest communication tools, and usually a quick option when network failures occur. Within this category, the results also demonstrate that DET are used for research by 72% of the sample. These results are aligned with literature that shows that traditional libraries are being partially replaced by digital search, specially by students who prefer it because they are easy and quick to search [ 25 ].

More than half of our sample identified the following benefits of using DET : the integration of multiple learning tools (66%), low cost (68%) and access without time or space limits (71%). Pinto and Leite [ 19 ] also mention that the use of DET has effects on the interaction time between students and teachers extending it beyond the traditional academic period. This access 24/7 can also be seen as a constraint and reduce teacher’s quality of life. However, considering the students’ perspective, it can be a major benefit. Männistö et al. [ 6 ] reported that the use of DET in nursing teaching enhances motivation for learning. When used in nursing education DET can improve the learning experience particularly in clinical learning settings [ 26 ]. Regarding low cost, there is no robust evidence that e-learning is a more cost-effective way to deliver knowledge when compared to traditional methods [ 27 ]. Yet, for students and teachers it may prove to be a more economical option as it avoids travel or food expenses.

Still regarding the benefits, no significant associations were identified between the teacher and student groups, except for the items: it allows the integrating of multiple learning tools (pdf, links, app, among others)” and “promotes student accountability”. These results are quite surprising because, given the age difference between the two groups in our sample, a more pronounced association would be expected. Literature shows that older adults are less likely to use technology that younger adults [ 28 , 29 ].

Our results show that constraints had greater statistical evidence than benefits. The more relevant were: the decreased interaction between students/teachers (72%), the less physical contact (77%), and the technical difficulties (67%). These aspects are pointed as the main barriers to the use of DET in literature and in fact, digital technologies can be used to enhance education, but they cannot completely replace face-to-face teaching [ 26 ]. Regarding technical difficulties, Naveed et al. [ 30 ] showed that efficient technology, infrastructure readiness and system reliability are some of the main critical success factors in implementing DET. To overcome these constrains several measures can be taken such as the use of combined methodologies (both presential and non-presential), a balanced and flexible schedule that maintains physical contact between students and teachers and permanent technical support available for users.

Implications identified by more than half of our sample include both the possibility of using software in simulation scenarios (57%) and the possibility to repeat simulations until learning, ensuring patient safety (55%). Overall, 66.9% of our sample consider that DET stimulates students’ independence (66%) and stimulate self-learning (71%). In nursing, the possibility to repeat simulations is identified as important and relevant for learning [ 31 ]; additionally, it is a significant factor for patient safety. Nevertheless, in our sample, only 14% considered that the use of DET improves performance in the execution of techniques. This could be due to a broader interpretation of the DET concept, as the technology by itself does not improve the practical skills required for nurses. Rather, the use of specific technology tools, such as clinical virtual simulation, can be used as a complementary strategy to DET, improving clinical reasoning skills [ 31 ].

The impact of DET on nursing care and patient outcomes is still poorly explored. Although there seems to be a link between improved nursing education and improved patient outcomes, this issue must be further explored. Additionally, there is a trend towards more centered and personalized models of care and the use of massive forms of education may not be suitable to this trend. It should be noted that some of the digital tools used in education are used in the health area, also with the aim of personalizing and, in this case, improving care. With regard to students, DET may be adapted to their educational and personal needs, which translates into a more student-centered teaching. Additionally, due to its positive impact, these technologies are being implemented in continuing education and professional development. A good example is the use of immersive technologies that allow nurses to have dynamic experiences with patients and be more prepared for real-world clinical settings. Nevertheless, the impact of DET on nursing care and patient outcomes is still poorly explored. Although, there seems to be a link between improved nursing education and improved patient outcomes, this issue should be further studied. It is also worth exploring if more student-centered teaching is later reflected in more people-centered nursing care.

Palvia et al. [ 32 ] state that e-education is advancing and is here to stay all around the world, which brings implications such as the need for improvement of telecommunications infrastructure, the acknowledgement of online education as equivalent to traditional face to face education and the globalization of e-education, which is inevitable, similar to the globalization of email or e-commerce. This authors also state that both online (virtual) and offline education must be combined so the virtues of both can be used.

It is necessary to have a more in-depth knowledge on this subject and a greater investment, both by teachers and educational institutions. Additionally, to understand the impact of the use of these technologies on nursing students learning, both in academic and clinical contexts, is needed. Nursing schools must invest in updated information technologies appropriate to the nursing curriculum, as well as providing training on their use to teachers and students. In our study, we verify that both teachers and students use mainly the tools made available by the school. Therefore, one way to improve the use of DET is to make more tools available. Additionally, in our setting the current teacher’s generation is more familiar with the first tools that were created and used as DET so those are the ones they mostly use. In our sample, most teachers do not have formal education on the use of DET and this should be included in the annual training program.

As to limitations this study was applied after the first lockdown that occurred in Portugal which triggered an exponential growth in the use of DET. As more periods of lockdown occurred both teachers and students were forced to improve the use of DET. Therefore, we consider that if the application of this study had occurred after these periods the results would have been different. Study design and type of sample are also limitations since our exploratory-descriptive study used a convenience sample and therefore results cannot be generalized.

5. Conclusions

Our results show that the use of DET in this sample is it is still at an early stage. Both students and teachers are still unfamiliar with the scope and possibilities of these tools, not taking full advantage of the potential they have to offer. The integration of multiple learning tools, low cost, and access without time or space limits are considered the main benefits of DET. On the other hand, the decreased interaction between students/teachers, the less physical contact, and the technical difficulties, are seen as the greater constraints. The main implications of DET are the possibility of using software in simulation, and the possibility to repeat simulations until learning, ensuring patient safety. It is also considered as a method that stimulates students’ independence and self-learning.

Although distance education through DET is a few years old, as far as nursing education is concerned, this concept is still very recent. Globalization and, more recently, the pandemic context that forced social isolation, have further boosted the introduction of DET in nursing education. Distance education thus becomes an effective strategy to reach people who want or need to be qualified, but who, for different reasons, cannot depart from their context of life and work [ 2 ]. Nevertheless, this constitutes a challenge to the nursing traditional teaching-learning methods, which have a predominantly practical and proximity component [ 27 ]. The distance between students and teachers, should be used to its full potential, involving students dynamically in the learning process, respecting independence, and autonomy, establishing links between learning and life and professional experience. On the one hand, it is necessary to provide teachers with skills to establish a link with students and stimulate their learning and engagement. It is also recommended that schools select the appropriate DET methods for teaching nursing, namely investing in robust digital platforms and state-of-the-art simulated practice technology [ 32 ]. The link between improved nursing education and its implication on nursing care must also be further explored.

Author Contributions

Conceptualization, F.L., L.S. and V.A.; methodology, F.L., L.S. and V.A.; software, F.L., L.S. and V.A.; validation, F.L., L.S. and V.A.; formal analysis, F.L., L.S. and V.A.; investigation, F.L. and V.A.; resources, F.L., L.S. and V.A.; data curation, F.L., L.S. and V.A.; writing—original draft preparation, F.L.; writing—review and editing, L.S. and V.A.; visualization, F.L., L.S. and V.A.; supervision, F.L., L.S. and V.A.; project administration, F.L. and V.A.; funding acquisition, F.L., L.S. and V.A. All authors have read and agreed to the published version of the manuscript.

This work is financed by national funds through the FCT—Foundation for Science and Technology, I.P., under the project UIDB/04585/2020. The researchers would like to thank the Centro de Investigação Interdisciplinar Egas Moniz (CiiEM) for the support provided for the publication of this article.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board and Ethics Committee of Escola Superior de Saúde Egas Moniz (reference number 881/2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Advancing Quantum Leadership and Community

Northwestern hosted a qed-c plenary meeting for academic, corporate, and government stakeholders.

Quantum 1.0 technologies — including lasers, MRI scanners, transistors, and semiconductor devices — paved the way for ubiquitous devices and services such as smart phones, laptops, and GPS navigation. Harnessing the quantum mechanics of sub-atomic particles — the phenomena of superposition, measurement, and entanglement — quantum 2.0 technology has the potential to revolutionize artificial intelligence, communications, information technology, manufacturing, and transportation and logistics.

To advance quantum research and technology development, Northwestern University is a founding member of the Quantum Economic Development Consortium (QED-C), an alliance of academic, corporate, and government stakeholders established under the 2018 National Quantum Initiative Act with support from the National Institute of Standards and Technology to accelerate the development of quantum information science and technology applications.

QED-C aims to realize the transformative potential of quantum 2.0 technologies — from quantum computing and cryptography to quantum sensing, timing, and imaging — by supporting a robust quantum ecosystem and quantum industry supply chain and identifying strategic gaps in enabling technologies, standards and regulation, and quantum workforce development.

On March 20-21, Northwestern hosted a QED-C plenary meeting for nearly 150 members — including leaders from Northwestern Engineering and the University —to learn, network, and identify collaboration opportunities.

Northwestern President Michael H. Schill and QED-C executive director Celia Merzbacher welcomed the guests.

“One doesn't need to be an expert about quantum computing to understand that it is a critical area for Northwestern and indeed for our nation and world,” said Schill, professor of law in Northwestern’s Pritzker School of Law and professor of finance and real estate in the Kellogg School of Management. “The research emerging from this field is poised to revolutionize our lives in the coming decades, and the work that all of you are involved in certainly brings needed solutions to a whole range of societal challenges that quantum computing will have the answers to.

“Quantum is a rising priority for Northwestern and for our faculty members. In the University priorities I unveiled last summer, I highlighted the importance of research and innovation in data science, artificial intelligence, sustainability, and decarbonization. All of those things are implicated in quantum computing, so I'm really excited that we're able to host this conference and that we're a founding member of this consortium.”

One doesn't need to be an expert about quantum computing to understand that it is a critical area for Northwestern and indeed for our nation and world. The research emerging from this field is poised to revolutionize our lives in the coming decades, and the work that all of you are involved in certainly brings needed solutions to a whole range of societal challenges that quantum computing will have the answers to.

Northwestern University President Michael H. Schill

Quantum computing for transportation and logistics

Quantum computing offers significant promise for optimization, real-time decision making, monitoring, and predictive modeling in the highly complex, data-driven transportation, logistics, and supply chain sectors.

On March 19, the QED-C Use Cases Technical Advisory Committee published a study, titled “ Quantum Computing for Transportation and Logistics ,” based on a workshop NUTC facilitated last October to assess the feasibility and impact of quantum computing use cases for transportation and logistics applications.

The committee — which also included NUTC senior associate director Bret Johnson ; Kevin Glynn , adjunct lecturer in Northwestern Engineering’s Master of Science in Information Technology Program ; and postdoctoral scholar Divyakant Tahlyan (MS ’21, PhD ’23) — proposed 83 potential uses, including applications with high-impact potential in the near-term such as demand forecasting and optimization of labor, routing, and warehousing.

At the QED-C plenary meeting, Glynn moderated a panel featuring Tahlyan, chief technology officer at PCS Software Yusuf Ozturk (CS PhD) and Catherine Potts of D-Wave Quantum to discuss the findings and recommendations of the study and the computational challenges within the transportation and logistics domains.

The panelists agreed that the existing level of classical computation power is insufficient to solve the algorithmic problems across the logistics and supply chain sectors and that the development and adoption of quantum computing technologies is the next step toward increasing operational efficiency, supply chain security and resilience, and efficiencies to address sustainability.

Sustainable quantum technologies

Mahmassani explained that quantum technologies can play a crucial role in enhancing sustainability efforts within transportation and logistics by optimizing energy consumption, reducing carbon emissions through more efficient routing, and improving overall resource utilization.

“What mRNA research did for COVID is similarly what quantum computing could do for climate change,” said Nivedita Arora , the Allen K. and Johnnie Cordell Breed Junior Professor of Design and assistant professor of electrical and computer engineering at Northwestern Engineering. “The next decade is going to be very critical, and we need to accelerate radical decarbonization efforts in electricity, transport, and manufacturing.”

Prem Kumar , professor of electrical and computer engineering, opened a dialogue in a QED-C plenary breakout session on both the applications of quantum to sustainability and the sustainability of quantum technology.

Kumar and panelist Michael R. Wasielewski are executive committee members of Northwestern’s Initiative for Quantum Information Research and Engineering (INQUIRE), a transdisciplinary hub of education and research excellence in quantum sciences across areas including material informatics and data science, material synthesis, molecular quantum transduction, nanotechnology, photonics, physics, and superconducting technologies.

“If we think that we're at an incipient stage of developing quantum technology, now is the time to really think of alternatives, to be creative and see what’s possible,” said Wasielewski, who is also the director of INQUIRE and the Center for Molecular Quantum Transduction .

Student focus on next-generation quantum technologies

Students conducting research in quantum fields from Northwestern and nearby universities were invited to participate in a special track at the plenary meeting which featured speed mentoring, a science communication workshop, and a poster session.

Gamze Gül , a fifth-year PhD student in applied physics advised by Kumar, is interested in designing quantum networking protocols to manage and control quantum networks using classical bits. She presented a poster titled “ Quantum Wrapper Networking ,” which demonstrated a novel approach to operate quantum networks that is both compatible with current fiber optic infrastructure and allows for quick adjustments to address network problems — such as loss or high traffic. By creating a package of quantum bits — or qubits — wrapped with classical bits, the qubits can be transported to their destinations without measurement or disturbance to the payload.

“Some technologies that we use in our daily lives, such as lasers, GPS, and transistors, would be impossible without a deeper understanding of quantum mechanics,” Gül said. “Today, we are entering a new era of quantum technologies that could help us understand the world we live in through quantum computing or sensing. It will be crucial to connect the distributed quantum systems.”

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U.S. Deputy Secretary of Energy David M. Turk and the Japan Minister of Education, Sports, Science and Technology, Masahito Moriyama, met in Washington, D.C., on April 9, 2024 on advancing fusion energy.

U.S. Deputy Secretary of Energy David M. Turk and the Japan Minister of Education, Sports, Science and Technology, Masahito Moriyama, met in Washington, D.C., on April 9, 2024 for a discussion on ways the nations can work together to advance fusion energy and released the following joint statement: 

The Department of Energy (DOE) of the United States of America and the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan have today decided to form a major new strategic partnership to accelerate the demonstration and commercialization of fusion energy. This new partnership intends to focus on advancing the U.S. Bold Decadal Vision for Commercial Fusion Energy and Strategy for International Partnerships in a New Era of Fusion Development , as well as the Japan Fusion Energy Innovation Strategy .

Fusion energy could provide a low-carbon, safe, sustainable and reliable energy supply with the potential to transform global efforts to achieve net-zero carbon emissions and to enhance energy security and resilience. This could deliver major societal and economic benefits. To fully realize these benefits, appropriate regulatory, social, and market policies are needed, alongside overcoming significant remaining technical challenges. 

The United States and Japan have a long history of collaborative activities in the area of fusion, including exchange programs, joint research and joint planning covered by the Coordinating Committee on Fusion Energy (CCFE), which is implemented under the Agreement between the Government of the United States of America and the Government of Japan on Cooperation in Research and Development in Science and Technology (the STA) 1 . The two countries are also participating in ITER, a large multi-national project that aims to bridge significant science and technology gaps toward the realization of fusion energy. 

This new partnership leverages the CCFE and is expected to further develop complementarity between U.S. and Japanese resources and facilities in fusion, including those in universities, national laboratories and private companies in the respective countries.  

This partnership is intended to focus on the following pillars: 

• Address the scientific and technical challenges of delivering commercially viable fusion energy for various fusion systems, through activities conducted pursuant to the STA; 
• Explore opportunities for shared access and/or development of facilities required for fusion research and development, and for how a coordinated, strategic approach can maximize value for the United States and Japan; 
• Promote the international harmonization of regulatory frameworks and codes and standards, including leveraging of rule-making efforts by the U.S. Nuclear Regulatory Commission as well as ongoing discussions under the auspices of the International Atomic Energy Agency and the Agile Nations Fusion Energy Working Group ;
• Identify and support the development of resilient global supply chains that facilitate commercial fusion deployment, welcoming and considering discussions among fusion industry groups in the respective countries;    
• Support public engagement with communities to facilitate a social license for deploying fusion energy and to support an equitable clean-energy transition; and 
• Promote skills development to ensure the people and talent growth necessary for the robust, inclusive and diverse workforce required by the fusion sector in the next decade and beyond.  

DOE and MEXT intend to drive the work of this strategic partnership through the CCFE, which is co-chaired by DOE and MEXT and is expected to meet in the coming months. The CCFE is expected to identify and delineate priority U.S.-Japan collaborative opportunities to realize our shared vision for fusion energy as an integral part of the world’s future energy supply. 

1 The CCFE was established in 1979 and is presently governed by the DOE-MEXT Implementing Arrangement Concerning Cooperation in Research and Development in Energy and Related Fields, which was signed in 2013 under the U.S.-Japan STA.

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You need to take courses in each of the three following Knowledge Domains in order to meet general education requirements:

• Creativity and Critical Analysis .
• Nature and Technology .
• Society and Culture .

By studying in these areas, you’ll establish a broad educational foundation. Your cultural, societal and scientific knowledge will be enhanced, and you’ll be better prepared to be a responsible citizen. 

You can meet the requirements by completing courses, with transfer credit or by passing exams.

Learn more about   Knowledge Domain requirements .

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For courses that have been approved for general education, include a statement identifying which of the three General Education Knowledge Domains (Creativity and Critical Analysis, Nature and Technology and Society and Culture) the course will help fulfill. The following statement was developed and approved by NIU’s General Education Committee March 24, 2022 and Baccalaureate Council Nov. 10, 2022.

“As part of the general education requirements, this course fulfills three credit hours in the Knowledge Domain of (_______) towards the minimum of 21 hours across the three domains. For more information on General Education Requirements, please see your advisor or review the ‘General Education Requirements’ for the appropriate  undergraduate catalog .”

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German university rescinds Jewish American’s job offer over pro-Palestinian letter

Nancy Fraser, professor of philosophy at the New School, condemned killings in Gaza carried out by the Israeli military

A leading Jewish American philosopher has been disinvited from taking up a prestigious professorship at the University of Cologne after signing a letter expressing solidarity with Palestinians and condemning the killings in Gaza carried out by Israeli forces.

Nancy Fraser, professor of philosophy and politics at the New School for Social Research in New York, said she had been cancelled by the university, which has withdrawn its invitation to the Albertus Magnus Professorship 2024, a visiting position, which she had been awarded in 2022. The letter was written in November 2023 following the 7 October attacks on Israel by Hamas, prompting Israel’s attack on Gaza.

Fellow academics have written a letter to the university in protest against the ban. In it, they call the withdrawal of the invitation “another attempt to limit public and academic debate on Israel and Palestine by invoking supposedly clear, distinct, governmentally sanctioned red lines”.

They said the letter, titled Philosophy for Palestine, which was signed by Fraser and several hundred other academics, was separate from Fraser’s work as a scholar and that her guest professorship had nothing to do with the Israel-Palestine conflict.

Cologne University said in a statement its decision to cancel the invitation had been made “with great regret”. It said the reason was that in the letter signed by Fraser, “Israel’s right to exist as an ‘ethno-supremacist state’ since its foundation in 1948 is called into question. The terror attacks by Hamas on Israel of 7 October 2023 is [sic] elevated to an act of legitimate resistance.”

It said that the signatories’ demand for the academic and cultural boycott of Israeli institutions was at odds with the university’s close ties to Israeli partner institutions and the views in the letter were not in line with its own statements from October 2022 on the situation in the Middle East region.

In an interview with the Frankfurter Rundschau , Fraser called herself a victim of “philosemitic McCarthyism” alongside a number of other academics such as Masha Gessen who have been cancelled in Germany over their views regarding the Middle East conflict amid growing criticism that a dominating pro-Israel political consensus has shut down any proper debate .

“After all, I was canceled in the name of German responsibility for the Holocaust. This responsibility should also apply to Jewish people. But in Germany it is narrowed down to the state policy of the currently ruling Israeli government. Philosemitic McCarthyism sums it up quite well. A way to silence people under the pretext of supposedly supporting Jews,” Fraser said.

In an interview with Die Zeit addressing the issue of Germany’s responsibility as perpetrator of the Holocaust for protecting Jewish life, Fraser said this duty was being wrongly applied to exclude criticism of the Israeli government.

“I completely agree that Germans have a special responsibility towards the Jews in light of the Holocaust. But to equate criticism of the Israeli government with antisemitism is simply wrong. And may I add that as a Jew I also feel a special responsibility. But … that doesn’t mean giving this government carte blanche. What is happening in Gaza should not happen – and especially not in my name. I strongly reject the equation of Israel and Judaism. Judaism has a rich secular and, above all, universalist tradition. It pains me when it is reduced to Israel’s current hyper-ethno-nationalist politics.”

Asked why she thought the philosopher Judith Butler had been allowed to assume the same professorship in 2016, despite having been highly critical of Israel, Fraser said: “In Germany, the panic of doing something wrong has increased. In addition the war in Gaza is now rekindling the feverishness.”

Fraser has said she will continue to hold the lectures she had planned to deliver in Cologne, both at the New School and at another location in Germany.

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“It has been suggested that I give the lectures elsewhere in Germany under the slogan: ‘This is what you weren’t allowed to hear in Cologne’.”

In a letter to Joybrato Mukherjee the rector of Cologne University, the interim president of the New School, Donna E Shalala, described his decision as “simply outrageous”, and “insulting”, and asked him to reconsider. She commented that Albertus Magnus, the 13th-century free-thinking philosopher, scientist and bishop after whom the professorship is named, “would have been appalled”.

In the 1930s, the New School, she pointed out, had “rescued intellectuals seeking refuge from the Nazis” – including the political theorist Hannah Arendt, the psychologist Erich Fromm and the composer Hanns Eisler.

“We continued the body of critical thought that had been wiped out – promoting the remarkable traditions of the German academy,” she wrote.

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