research on engineering design process

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Published: 08 January 2021

Effects of infusing the engineering design process into STEM project-based learning to develop preservice technology teachers’ engineering design thinking

Kuen-Yi Lin ORCID: orcid.org/0000-0002-6250-0540 1 ,
Ying-Tien Wu 2 ,
Yi-Ting Hsu 3 &
P. John Williams 4

International Journal of STEM Education volume 8 , Article number: 1 ( 2021 ) Cite this article

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This study focuses on probing preservice technology teachers’ cognitive structures and how they construct engineering design in technology-learning activities and explores the effects of infusing an engineering design process into science, technology, engineering, and mathematics (STEM) project-based learning to develop preservice technology teachers’ cognitive structures for engineering design thinking.

The study employed a quasi-experimental design, and twenty-eight preservice technology teachers participated in the teaching experiment. The flow-map method and metalistening technique were utilized to enable preservice technology teachers to create flow maps of engineering design, and a chi-square test was employed to analyze the data. The results suggest that (1) applying the engineering design process to STEM project-based learning is beneficial for developing preservice technology teachers’ schema of design thinking, especially with respect to clarifying the problem, generating ideas, modeling, and feasibility analysis, and (2) it is important to encourage teachers to further explore the systematic concepts of engineering design thinking and expand their abilities by merging the engineering design process into STEM project-based learning.

Conclusions

The findings of this study provide initial evidence on the effects of infusing the engineering design process into STEM project-based learning to develop preservice technology teachers’ engineering design thinking. However, further work should focus on exploring how to overcome the weaknesses of preservice technology teachers’ engineering design thinking by adding a few elements of engineering design thinking pedagogy, e.g., designing learning activities that are relevant to real life.

Introduction

An increasing number of position papers and empirical studies have focused on exploring the issues of engineering design thinking (Brand, 2020 ; English & King, 2015 ). With regard to the cognitive structure of technology teachers in engineering design, Atman et al. ( 2007 ) studied the differences between expert practitioners and students during the engineering design process. They proposed that students should pay more attention to problem scoping, information gathering, and decision-making as they develop their cognitive structures and schematic processes in engineering design. Furthermore, when Hynes ( 2012 ) investigated how middle school teachers understood and taught the process of engineering design, he found that technology teachers frequently exhibit sophisticated thinking during two particular steps of engineering design: constructing a prototype and redesigning. In a study about the performance of high school students in engineering design subjects, Fan, Yu, and Lou ( 2018 ) noted that students’ abilities in predictive analysis and testing/revising are key factors in determining their thinking in engineering design. Sung and Kelley ( 2018 ) performed a sequential analysis study on the design thinking of fourth-grade elementary students and found that idea generation plays an important role throughout their design thinking process. These studies suggest that students and teachers at different levels focus on different parts of the design process: idea generation is the most important part of the process for elementary school students; predictive analysis and testing/revising are more important for high school students; and technology teachers tend to focus on prototype construction and redesign. Therefore, the differences between engineering experts and beginners in the engineering design process do not necessarily correspond to what technology teachers emphasize in their engineering design teaching. Furthermore, there is a lack of research on the cognitive structures and engineering design foci of preservice technology teachers, which is an important issue that needs to be addressed.

In addition to engineering design thinking, science, technology, engineering, and mathematics (STEM) education has also received considerable attention in recent years. The notion of STEM itself and how its disciplines should be integrated are open to debate (English & King, 2019 ), and attracting appropriately qualified people to study and work in STEM areas is an urgent need (Holmes, Gore, Smith, & Lloyd, 2017 ). Various definitions of STEM education have ranged from disciplinary to transdisciplinary approaches, but from a broad perspective, it can be defined as follows: “STEM education is used to identify activities involving any of the four areas, a STEM-related course, or an interconnected or integrated program of study” (English, 2017 ). Strimel and Grubbs ( 2016 ) noted that technology and engineering are often neglected in secondary-school STEM education, and this neglect perpetuates the educational system’s shortcomings in nurturing technology and engineering talent. In view of this problem, Song et al. ( 2016 ) and Brophy, Klein, Portsmore, and Rogers ( 2008 ) agreed that STEM education implementation can be improved by planning technology-learning activities (such as hands-on activities) that incorporate engineering design so that students can obtain comprehensive cross-disciplinary experience. That is, students will have more chances to apply STEM knowledge and competency in solving problems or meeting needs instead of focusing on learning specific subject matter and neglecting the application of that knowledge (Lin, Hsiao, Williams, & Chen, 2020 ). In addition, many studies have found that technology-learning activities based on engineering design enhance learning in STEM. For example, English and King ( 2019 ) reported students’ responses to designing and constructing a paper bridge that could withstand an optimal load. Their results showed that students’ sketches indicated an awareness of the problem constraints, an understanding of basic engineering principles, and the application of mathematics and science knowledge.

Based on this line of reasoning, technology-learning activities that incorporate engineering design should be useful for implementing STEM education. However, the implementation of teaching activities oriented toward engineering design requires teachers who have a strong conceptual understanding of engineering design. Therefore, investigations of this aspect are important from an academic and practical perspective. For example, in studies about engineering design, some common concerns are the cognitive structures of expert practitioners in engineering design (Atman et al., 2007 ) and the characterization of the engineering design process (Hannah, Joshi, & Summers, 2012 ); the findings of these studies are usually applied in different educational settings (Capobianco & Rupp, 2014 ; Fan, Yu, & Lin, 2020 ). Therefore, if technology teachers are to implement STEM education using the processes of technology-learning activities based upon engineering design, the teachers’ own cognitive structure of engineering design is of great significance. A cognitive structure is a hypothetical construct showing the extent of concepts and their relationships in a learner’s long-term memory (Shavelson, 1974 ). Through probing technology teachers’ cognitive structures, technology teacher educators can understand what knowledge technology teachers have already acquired. In addition, it is a fundamental step toward acquiring evidence of technology teachers’ cognitive structure in order to explore how technology teachers construct engineering design in technology-learning activities (Wu & Tsai, 2005 ).

One of the primary objectives of the Taiwan Technology Curriculum in 12-year compulsory education is to ensure that secondary school students possess basic competencies in engineering design thinking before they enter university-level engineering institutes (Ministry of Education, 2018 ). This sounds like a prevocational approach, as engineering design thinking is relevant only to students who progress to engineering education. If the Technology Curriculum is a general curriculum for all students, then the rationale should be that engineering design thinking is good for all students. The aforementioned studies suggest that the cognitive structures of technology teachers in engineering design will affect their use of technological pedagogical approaches oriented toward engineering design and thus determine the quality of STEM education implementation. To address this gap in the literature, this study focuses on the cognitive structure of preservice technology teachers in engineering design to probe their understanding of engineering design thinking. We will describe the current status and issues related to the way preservice technology teachers apply cognitive structure to engineering design. The findings of this study will help preservice technology teachers incorporate engineering design processes into their technology teaching activities. This, in turn, could foster engineering design capabilities in secondary school students and their interest in engineering-related areas. More specifically, the primary research questions of this study are as follows: (1) How does the incorporation of engineering design processes into STEM project-based learning benefit the training of preservice technology teachers in terms of their cognitive structure in engineering design thinking? (2) What are the weaknesses in the cognitive structure of preservice technology teachers in engineering design thinking?

Theoretical framework

To explore the gap in engineering design thinking in the literature, the following literature review focuses on exploring engineering design thinking and related studies. Furthermore, to develop the theoretical basis for STEM project-based learning combined with the engineering design process, the following literature review also focuses on analyzing the related studies and proposing the key elements in developing a STEM project learning curriculum.

Engineering design thinking

Many scholars in the field of technology and engineering education believe that engineering design thinking is a basic competency in engineering and that this mode of thinking should be given priority in secondary and tertiary education (Atman et al., 2007 ; Dym, Agogino, Eris, Frey, & Leifer, 2005 ). According to Dym et al. ( 2005 ), “Engineering design is a systematic, intelligent process in which designers generate, evaluate, and specify concepts for devices, systems, or processes whose form and function achieve clients’ objectives or users’ needs while satisfying a specified set of constraints.” The cultivation of engineering design thinking can encourage students to develop an inquisitive mindset, approach problems from multiple perspectives, and question existing norms.

Recent studies on engineering design thinking have generally focused on either the processes of this form of thinking or the difference between engineering design experts and university-level engineering students in terms of engineering design thinking. A variety of design thinking processes have been proposed by researchers in this area. Atman et al. ( 2007 ) proposed that the engineering design process proceeds as follows: (1) problem definition, (2) information gathering, (3) idea generation, (4) modeling, (5) feasibility analysis, (6) evaluation, (7) decision, (8) communication, (9) implementation, and (10) design revision. Similarly, Hynes ( 2012 ) proposed a slightly different engineering design process: (1) identify need or problem, (2) research need or problem, (3) develop possible solutions, (4) select best possible solution, (5) construct a prototype, (6) test and evaluate solution, (7) communicate the solution, and (8) redesign. The main differences between the engineering design thinking process and the problem-solving process are a greater emphasis on modeling and feasibility analysis in the former. In other words, in the problem-solving process, students may not as thoroughly evaluate the viability of each idea when choosing the optimal solution. However, the appropriate use of modeling and feasibility analyses can improve students’ capacity to evaluate ideas, and this is one of the most important features of the engineering design thinking process.

The engineering design thinking processes proposed by Atman, Cardella, Turns, and Adams ( 2005 ) and Hynes ( 2012 ) include a communication step after the decision step. Even after modeling and feasibility analyses have been conducted and an optimal solution has been selected, it is still necessary to communicate with a client to confirm the final solution and make further adjustments to the solution to address any questions or problems. The purpose of this step is to confirm that a client’s needs are met and to ensure that all team members understand the final solution. The above analysis also shows that there are certain differences between design thinking processes and problem-solving processes.

Research on engineering design thinking

Interested in cultivating engineering design thinking in university and secondary school students (Wind, Alemdar, Lingle, Moore, & Asilkalkan, 2019 ), many researchers have examined the differences in design thinking processes between expert and novice engineers when faced with an engineering problem. For example, Atman et al. ( 2005 ), Atman et al. ( 2007 ), and Lammi and Becker ( 2013 ) used engineering problems such as the design of a playground for a fictitious neighborhood and the construction of a ping-pong-ball launcher to investigate the engineering design thinking of engineering experts and students. The most important findings of these studies are as follows: (1) expert engineers ask many more questions than students do during the problem-definition step, and some of these questions (15%) are highly specific and based on close scrutiny; (2) during the information-gathering step, expert engineers collect vast amounts of differentiated data, whereas beginners gather only small amounts of localized data and spend a relatively small amount of time on this step; and (3) during the decision step, expert engineers use mathematical calculations and theoretical methods to support their decisions, whereas beginners tend to rely more on intuition.

In a study on the engineering design processes of expert engineers, high school freshmen, and high school seniors, Atman et al. ( 2007 ) found that experts spend significantly more time on the information gathering, feasibility analysis, evaluation, and decision-making steps than high school students do. They also found that high school seniors spend more time on idea generation, feasibility analysis, and decision-making than high school freshmen do. Thus, this study revealed differences between experts and high school students in engineering design processes. Although the study focused mainly on analyzing the amount of time spent on each step of the engineering task, it quantified some of the differences between experts and high school students in engineering design processes. In studies about technology and engineering teachers, Hynes ( 2012 ) examined the understanding and teaching of the engineering design process by middle school teachers. The most significant finding of this study was that technology teachers often display sophisticated thinking during two particular steps of the engineering design process—constructing a prototype and redesigning.

Overall, expert engineers usually spend more time on each step of the design process than students do; technology teachers generally apply complex thinking while creating prototypes during redesign; and experts ask more questions, perform more research, and conduct more testing than students. The implication is that experience leads to a more mature engineering design thought process. It is important to pay attention to these findings when nurturing future engineering design talent. Despite these related studies, it is not enough for research on engineering design thinking to explore the engineering design process of expert or novice engineers. If we wish to empower teachers to feel more confident in developing and delivering robust engineering-related curricula, we must first explore teachers’ existing cognitive structures. Therefore, the definitions, system concepts, examples, and advanced system-concept explanations of the engineering design process are explored in this study.

STEM project-based learning combined with engineering design

Many engineering design studies have considered how research findings can be converted into tools for engineering pedagogy to provide an education that nurtures the cognitive skills of students and their ability to fuse theory and practice (Borgford-Parnell, Deibel, & Atman, 2010 ). For the purpose of this study, STEM project-based learning combined with the engineering design process (EDP-STEM-PBL) may be described as a mode of pedagogy that purposefully situates scientific and mathematical knowledge within the context of technological design to create a problem-solving learning environment in which students envision solutions to design challenges, gather information, and solve real problems through the use of the engineering design process (Sanders, 2009 ; Wahono, Lin, & Chang, 2020 ). This approach is expected to improve student competitiveness in the burgeoning knowledge economy. STEM education has already been emphasized in education systems in the USA. Bybee ( 2013 ) stressed that integrative STEM education should pay attention to globally important issues (e.g., climate change, energy sources) and develop design capabilities through practical technology and engineering activities, paying special attention to theory-based design. The aim of this cross-disciplinary approach is to simultaneously teach rigorous academic concepts and provide experiential, real-world learning opportunities. Therefore, EDP-STEM-PBL can be used to systematically cultivate the scientific, technological, mathematical, and engineering knowledge of students through the engineering design thinking process, thus expanding students’ perspectives and mitigating the lack of practicality in conventional pedagogy, which tends to overemphasize theoretical learning. EDP-STEM-PBL repurposes engineering design studies into pedagogical tools for teaching engineering design.

In conventional pedagogy, intuition is often used to solve problems. However, applying analytical strategies and explicit step-by-step processes to ordinary problems often results in better solutions (Baumann & Kuhl, 2002 ). Therefore, EDP-STEM-PBL calls for the formation of learning groups that take full responsibility for their learning, and learning goals are achieved through cooperation and sharing among team members (Milentijevic, Ciric, & Vojinovic, 2008 ). In this study, the engineering design process proposed by Atman et al. ( 2007 ) was used to develop STEM project-based learning activities, with an emphasis on the steps of prototype construction and redesign, as suggested by Hynes ( 2012 ). In this way, we hope to cultivate the engineering design skills needed by preservice technology teachers and to facilitate sophisticated thinking skills in preservice teachers.

Research methods

Research design.

To investigate the effects of EDP-STEM-PBL on the engineering design thinking of preservice technology teachers, we used a pretest-posttest nonequivalent groups design (see Table 1 ). The STEM project used in this study was the mousetrap car. All preservice technology teachers received 12 periods (50 min/period) of mousetrap car activity training courses over 6 weeks. The courses were intended to improve the preservice teachers’ understanding of STEM knowledge about mousetrap cars, e.g., friction, Newton’s first law of motion, material processing, engineering graphics, the definition and process of engineering design, and the Pythagorean theorem. They were then asked to design and construct a mousetrap car that could travel more than 10 m on a 1-m-wide track. During the activity, the experimental group was taught using an EDP-STEM-PBL curriculum, and the control group was taught using a STEM-PBL curriculum based on the technological problem-solving process (PS-STEM-PBL). The differences between the two curricula are described below.

Since this study focused mainly on the cognitive structure used by preservice technology teachers in engineering design thinking, this study followed Tsai and Huang’s ( 2002 ) suggestion in using the flow-map method to explore preservice technology teachers’ cognitive structure and avoid the limitations of free word association, controlled work association, tree construction, and concept mapping. The research subjects (preservice technology teachers) were interviewed before and after the experimental teaching course; that is, for the pretest, the preservice teachers were interviewed before the 12 periods of mousetrap car activity training courses, and for the posttest, they were interviewed after the training courses. The flow-map method was then used to analyze their cognitive structures in engineering design thinking, enabling us to identify how the preservice technology teachers developed and changed their cognitive approaches to engineering design thinking through the experimental curriculum. Finally, the learning performance of the preservice technology teachers in the experimental and control groups was examined by employing the flow-map method to analyze the engineering design thinking of the research subjects from each group. In this way, our study provided insight into how different curricula affected the actual performance of preservice technology teachers in a STEM-PBL environment. We used these findings to develop recommendations for EDP-STEM-PBL pedagogy that can serve as a reference for teaching science and technology in the future.

There were similarities and differences between the “engineering design process” and “problem-solving process” curricula as taught to the experimental and control groups.

The researcher was responsible for designing and teaching the EDP group and PS group curriculum. This curriculum is one of the major units of “Introduction to Industrial Technology Education,” but the students’ performances do not influence their final grades. The researcher introduced the background of this study and the definition and application of the engineering design process to both groups to enable them to understand the importance of applying the EDP in activity. The experimental group’s engineering design process curriculum (EDP-STEM-PBL) included such engineering design processes as modeling, feasibility analysis, and group communication; the curriculum began with information gathering according to the problem definition, followed by feasibility analyses based on the problem constraints and then the selection of a solution and construction of a prototype. In contrast, the control group’s technological problem-solving process included problem definition, data collection, feasible idea development, best idea selection, best idea implementation, results evaluation, and design idea revision. The process began with the development of knowledge and problem-solving skills that created a link between the problem and the students’ cognitive structures; this was followed by experimental analysis to verify the students’ hypotheses.

Problem framework

In the experimental group (EDP-STEM-PBL), the problem features were described in a qualitative and highly detailed manner to encourage a search for the most feasible solution. Additionally, this curriculum provided an integrated, cross-disciplinary learning experience (science, technology, engineering, and mathematics) to help the subjects develop high-level cognitive abilities (e.g., design, innovation, and critical thinking) in STEM areas. In the control group (PS-STEM-PBL), the problems were addressed using existing experience, concepts, and techniques in conjunction with a variety of ideas, leading the subjects to find and implement solutions appropriate to the current problem.

Research subjects

The subjects of this study were 28 freshmen/preservice technology teachers who were being trained at the National Taiwan Normal University to specialize in technology education. They had similar engineering-related experience in their pre-engineering technology education courses at the senior high school level due to the national technology curriculum guidelines. All preservice technology teachers must take a required course named “Introduction to Industrial Technology Education.” To ensure that they understood the importance of hands-on learning, a mousetrap car activity was arranged in this course as a key technology-learning activity (see Fig. 1 ). Fifteen preservice technology teachers were randomly assigned to the experimental group and taught using an EDP-STEM-PBL curriculum, and the thirteen remaining teachers were randomly assigned to the control group and taught using a PS-STEM-PBL curriculum. The National Taiwan Normal University (NTNU) was selected as the site of this study because it was mainly a secondary school technology teacher training university before it diversified its teacher development programs. The NTNU has a long history of teacher education, and it is presently the main source of secondary school technology teachers in Taiwan. Hence, conducting our study at this university could directly improve teacher education curricula and help equip preservice teachers with engineering design thinking capabilities.

Example of a mousetrap car

Research tools

In representing individual cognitive structure, the flow-map method may be the most useful method for representing the cognitive structure (Tsai & Huang, 2002 ). Anderson and Demetrius ( 1993 ) argued that the flow-map method requires minimal intervention by the interviewer and little inference in its construction (Wu & Tsai, 2005 ). In this study, we conducted in-depth, semistructured interviews to enable us to use the flow-map method to characterize the cognitive structures of preservice teachers in terms of engineering design thinking. After the audio-recorded interview, a “metalistening” technique was followed for the purpose of exploring the preservice technology teachers’ additional conceptual knowledge. The researchers could immediately replay the interview recordings to provide an opportunity for the preservice technology teachers to recall additional concepts of engineering design that they had not previously disclosed. The preservice technology teachers’ responses to the metalistening technique were also audio recorded by a second recorder. Thus, the interviews could be transcribed verbatim to produce a flow map representing the cognitive structures of the preservice technology teachers in this study (Wu & Tsai, 2005 ). The interview questions in this study were largely based on the questions designed by Tsai and Huang ( 2002 ) for in-depth, semistructured interviews. We used the following questions to probe the subjects’ cognitive structures about engineering design thinking: (1) Could you tell me whether you have used the engineering design thinking process to solve an engineering problem? (2) Please tell me more about the concepts you have just mentioned. (3) Could you explain how the concepts you mentioned are connected to each other? (4) Do you have anything to add to that explanation? To ensure that this research tool was effective, we drafted the interview questions according to the research tools of other, related studies; invited experts on the flow-map method to check our questions; and revised our questions accordingly.

Data analysis

Both quantitative and qualitative data analyses were performed in this study. First, we drafted a flow map based on the interview recordings, and then, after discussion and analysis of the flow map through consensus evaluation, we conducted a quantitative analysis of the flow map to identify gaps and weaknesses in the cognitive structures of preservice technology teachers in engineering design thinking. The data analysis steps of the flow maps were as follows: (1) we interviewed the preservice technology teachers and produced the interview recordings, coupled with the metalistening technique; (2) the interviews were transcribed verbatim into flow maps based on the process proposed by Wu and Tsai ( 2005 ); and (3) two researchers adopted the consensus evaluation method in analyzing the flow maps and producing the quantitative data of the preservice technology teachers’ performance in engineering design thinking, which included the following criteria: (1) defining engineering design thinking: a correct definition is assigned a value of 1, and an incorrect or no definition is assigned a value of 0; (2) overall system concepts in engineering design thinking: two researchers discussed the flow maps, decided which steps had been mentioned, and calculated the total number of steps; (3) individual system concepts in engineering design thinking; the two researchers discussed the flow maps and decided which steps had been mentioned. Steps that were mentioned are assigned a value of 1; otherwise, the value is 0; (4) providing examples of engineering design thinking: providing correct examples is assigned a value of 1, and incorrect or no examples are assigned a value of 0; (5) explanations of advanced system concepts (overall): the two researchers discussed the flow maps, decided which steps had further explanations and calculated the total number of steps; (6) explaining system concepts (individual): the two researchers discussed the flow maps and decided which steps had further explanations. Steps that were mentioned are assigned a value of 1; otherwise, the value is 0. By applying the consensus evaluation, the two researchers could come to an exact agreement on the preservice technology teachers’ performance in engineering design thinking and share a common interpretation of the construct (Stemler, 2004 ).

In summary, the interviews were transcribed verbatim, and a flow map (see Figs. 2 , 3 , and 4 ) was drafted using this verbatim transcript (Tsai & Huang, 2002 ). To analyze the flow maps, the two researchers involved in this work conducted a consensus evaluation on the flow maps and discussed the verbatim transcripts of the preservice technology teachers’ interviews in their flow maps to assess the subjects’ performance in relation to definitions, system concepts, examples, and advanced system-concept explanations of engineering design thinking and generate quantitative data. “Definitions” refers to the ability of a preservice technology teacher (PTT) to clearly explain the meaning of the engineering design process. “System concepts” refers to a PTT’s procedural knowledge (i.e., ability to explain each step of the engineering design process). “Examples” refers to the ability of a PTT to provide examples of each step in the engineering design process and the application of these steps. “Advanced system-concept explanations” refers to the ability of a PTT to use correct conceptual knowledge to explain how the engineering design process is used to solve a problem. In addition, to analyze the differences between the experimental and control groups, we conducted a chi-square test to compare their cognitive structures and suppress differences before the experiment.

ED9 flow map: pretest interview

PS6’s flow map: pretest interview

PS6 flow map: posttest interview

In the following sections, we describe the results of this study in terms of the definitions, system concepts, examples, and advanced system-concept explanations of the preservice technology teachers as they relate to the engineering design thinking process.

Preservice technology teachers’ performance: defining engineering design thinking

As shown in Table 2 , the number of preservice technology teachers in the engineering design group (experimental group) who were able to define engineering design thinking increased from two in the pre-experiment test to eight in the post-experiment test. However, the number of preservice technology teachers in the problem-solving group (control group) who were able to provide a definition of engineering design thinking increased only from four in the pretest to eight in the posttest.

Preservice technology teachers’ performance: system concepts in engineering design thinking

Overall system concept.

An analysis of the performance of the preservice technology teachers in terms of their overall system concepts of engineering design thinking is shown in Table 3 . A chi-square test indicated that there was a statistically significant difference between the engineering design group (experimental group) and problem-solving group (control group) in this regard.

Individual system concepts

An analysis of the performance of the preservice technology teachers in each system concept of engineering design thinking is shown in Table 4 . According to the chi-square test, there were statistically significant differences between the experimental and control groups in four system concepts: problem definition, idea generation, modeling, and feasibility analysis.

Preservice technology teachers’ performance: providing examples of engineering design thinking

As shown in Table 5 , the number of preservice technology teachers in the experimental and control groups who were able to provide examples of engineering design thinking increased from 5 and 3 in the pre-experiment test to 15 and 10 in the post-experiment test, respectively. The chi-square test indicated that there was a statistically significant difference between the experimental and control groups.

Preservice technology teachers’ performance: explanations of advanced system concepts

Ability to provide explanations for advanced system concepts.

An analysis of the abilities of the preservice technology teachers to provide explanations for advanced system concepts is shown in Table 6 . According to the chi-square test, there was a statistically significant difference between the experimental and control groups in this aspect.

Ability to provide advanced explanations for each system concept

Table 7 shows an analysis of the preservice technology teachers’ ability to provide advanced explanations for each system concept. The chi-square test showed that there was a statistically significant difference between the experimental and control groups in terms of their ability to provide advanced explanations for idea generation.

This section discusses a few important topics in further depth in light of the above results. The topics include the findings of Hynes ( 2012 ), who found that technology teachers tend to focus on prototype construction and redesign when they teach engineering design, and whether EDP-STEM-PBL helps to improve the thinking capabilities of preservice teachers in these steps. In addition, we discuss whether preservice teachers are able to exhibit sophisticated thinking during the engineering design thinking process.

Does EDP-STEM-PBL improve preservice technology teachers’ performance during modeling?

In the engineering design process proposed by Hynes ( 2012 ), the most important step of the process for preservice teachers is prototype construction. Hynes ( 2012 ) further noted that preservice teachers should be able to construct ideas and perform calculations. Idea construction refers to the ability of a preservice teacher to evaluate the feasibility of a model and deeply understand the scope of effects brought about by a modification to the model after the idea generation step in EDP-STEM-PBL. The ability to perform calculations refers to the ability of a PTT to come to terms with the complex or custom dimensions of the prototype constructed according to the idea generation, including the ability to understand the characteristics and measurements of the prototype. In the following sections, we will describe how the preservice teachers in this study reflected on their learning after they were taught the EDP-STEM-PBL curriculum.

The prototype construction step was described by Hynes ( 2012 ) as the construction of a working model of an original idea. During this step, an idea may be further developed according to the features of the prototype, and the feasibility of some desired change to the prototype’s specifications may also be assessed. The performance of the preservice technology teachers during the prototype construction step (as per the definition above) is shown in Table 4 . When we performed an analysis on the individual system concepts that are key for project-based learning, we found statistically significant differences between the experimental and control groups. This indicated that the preservice teachers in the experimental group were able to clearly and correctly explain procedural knowledge of prototype construction. It also showed that these teachers tended to think that the problems that occur while generating ideas and performing calculations affect the quality of the final implementation. In other words, most of the preservice technology teachers in the experimental group tried to improve their designs after evaluating the prototype they constructed according to the objectives and standards, having gained insights that enabled them to refine the final implementation. In contrast, the performance of the control group in terms of implementation quality was directly affected by the problem-definition and information-gathering steps. The control group was unable to attain the same high level of performance as those using the engineering design process (experimental group) during prototype construction.

Does EDP-STEM-PBL improve preservice technology teachers’ performance during redesign?

In Hynes’s ( 2012 ) definition of the engineering design process, redesign refers to the learners’ ability to understand the goals of a project and to realize that the project implementation does not have to be perfect after the first iteration; it is also a measure of the learners’ ability to learn from mistakes and validate their designs. Explanations of redesign may be categorized as in situ explanations, review explanations, and advanced explanations. In situ explanations refer to explanations of redesign provided by a teacher while the student is learning how to build a project (e.g., modifications and suggestions). Review explanations refer to guidance provided by teachers to students after the students have already finished most of the redesign. Advanced explanations refer to advanced interpretations of the redesign, such as designers retracing their work to the problem-definition step (e.g., to identify newly recognized needs or problems) or returning to the modeling step (e.g., to improve their models for a new version of the design). The redesign step can be thought of as preservice technology teachers fundamentally rethinking the engineering design process and overhauling their designs.

As shown in Table 4 , the performance of the preservice technology teachers in the experimental group ( N = 4, 27%) was better than that of the preservice technology teachers in the control group ( N = 1, 8%). This implies that the preservice technology teachers who were taught using the EDP-STEM-PBL curriculum were better than those in the control group at implementing their projects using the engineering design process in an orderly manner. Based on the results of the interviews, teachers should use in situ and review explanations to help students understand redesign and thus expound on the contents of and knowledge within the curriculum. Furthermore, teachers should highlight the actions that must be taken by the students and prevent them from taking ineffective actions. With the teachers’ guidance, the students should think of the engineering design process as a whole instead of rushing to complete the project. Furthermore, students should be guided through advanced explanations to return to the problem-definition or modeling step during redesign to think about why it may be necessary to revise or improve their original design. The overall results suggested that pedagogy based on the engineering design process can significantly improve the redesign capabilities of preservice technology teachers in STEM-PBL.

Does EDP-STEM-PBL improve preservice technology teachers’ ability to use sophisticated thinking?

The cultivation of sophisticated thinking in the engineering design process can be discussed from two perspectives: the ability to provide examples (Table 5 ) and advanced system concept explanations (Table 6 ) about the engineering design process. Our analyses showed that the experimental group was superior to the control group in providing examples of engineering design thinking. This suggests that EDP-STEM-PBL helps cultivate the ability of preservice technology teachers to think divergently or convergently. Atman et al. ( 2007 ) noted that expert engineers are skilled at making decisions about engineering-related concepts because they have absorbed a broad and diverse range of engineering concepts. Based on their working experience in various jobs, professional engineers are able to easily and accurately evaluate components of the engineering design process (e.g., problem descriptions, prototypes, work plans, independent completion time). They are highly adept at applying their metacognitive skills and understanding engineering design thinking concepts.

The aforementioned qualitative analyses also showed that the experimental and control groups were both unable to provide advanced system concept explanations during project implementation. This is somewhat inconsistent with the findings of previous studies. For example, in a study in which freshmen and senior engineering students were asked to perform an engineering task, Atman et al. ( 2005 ) found that the seniors had a higher number of transitions, greater progression to later stages, and longer design times than the freshmen (problem-solving time is an important factor, as it encompasses the cognitive structures of advanced system concepts in engineering design thinking). West, Fensham, and Garrard ( 1985 ) noted that “cognitive structures” generally consist of two important components: the knowledge stored within the conceptual structure and the organization of this knowledge. It is possible that the preservice technology teachers in this study were not able to fully express their advanced system-concept explanations during the STEM-PBL curricula, thus resulting in a deviation from the findings of previous studies. In our opinion, the preservice technology teachers in the experimental and control groups may have spent too much time on problem definition and were slow to transition to developing alternative solutions and project implementation. Atman et al. ( 2005 ), who used similar teaching materials, found that freshmen and senior engineering students both spent large amounts of time on the problem-definition step, which includes the time spent reading and describing the problem. Therefore, we believe that the preservice technology teachers who were taught using EDP-STEM-PBL were highly proficient in presenting basic descriptions and definitions of engineering design thinking. However, they were far less proficient in the use of high-level deductive reasoning and dialectical thinking, as they generally preferred to use basic definitions, descriptions, and conceptual explanations rather than exhibiting higher-level skills. It is possible that the preservice technology teachers who were taught with the EDP-STEM-PBL curriculum will gradually transition to higher-level skills as they become more familiar with engineering design process pedagogy.

Research limitations

This study focuses on the cognitive structure of preservice technology teachers in engineering design to probe their understanding of engineering design thinking. However, a self-criticism of this study reveals that it was subject to the following limitations. The utilization of the flow-map method with the metalistening technique is very time consuming, making it difficult to increase the number of research subjects. That is, the external validity (generalizability) of this study is the major limitation.

Another limitation is the statistical approach to exploring the effect of applying the engineering design process to STEM project-based learning in developing preservice technology teachers’ schema of design thinking. If the number of research subjects had exceeded 30 preservice technology teachers in each group, we could have utilized analysis of covariance (ANCOVA) in this study. However, there were only 28 preservice technology teachers in this study; thus, we could utilize only the chi-square test to compare their cognitive structures and suppress differences before the experiment.

Finally, we arranged 1 h of interview time for each preservice technology teacher for the purpose of controlling the interview time. That is, the subjects had to express what they wanted to say within 30 min and then listen what they had said and explain what they had forgotten to say for the remaining 30 min. In the pretest interview, this amount of time was sufficient. However, for the posttest interview, this amount of time was sometimes insufficient. Very few preservice technology teachers were anxious to extend the interview, even when we asked them to feel free to finish what they wanted to say.

The main focus of this study was the effectiveness of EDP-STEM-PBL in cultivating the cognitive structures of preservice technology teachers with respect to engineering design thinking. Based on the experiment, we analyzed the effects of EDP-STEM-PBL and compared the results of the experimental and control groups. The results of the experimental teaching activity are as follows:

EDP-STEM-PBL improved problem definition, idea generation, and engineering design thinking

First, preservice technology teachers who were taught using the engineering design thinking process (the experimental group) performed better than the control group in problem definition, idea generation, modeling, and feasibility analysis. In this study, 28 preservice technology teachers were taught from a STEM-PBL curriculum based on the mousetrap car project. The number of preservice technology teachers who were able to describe the problem increased from two (13.3%) to eight (53.3%) in the experimental group and from four (30.8%) to five (38.5%) in the control group. Hence, the ability of the experimental group to define the basic problem (i.e., the ability to clarify the scope and context of the problem) improved significantly after EDP-STEM-PBL. This is consistent with the findings of Atman et al. ( 2007 ), whose subjects were asked to design a playground: expert engineers generally read a problem several times and ask for the specifics to be repeated, which helps them identify the constraints of the problem, reconstruct the problem, and summarize effective ideas. In other words, the experimental group was better able to define how the problems of the project activity were linked to the goals of the project after they were taught the EDP-STEM-PBL curriculum.

Atman et al. ( 2007 ) noted that expert engineers spend large amounts of time on the engineering design thinking process. However, during the teaching experiment in this study, we found that the subjects either used their intuition or convergent/logical thinking to solve the problem after the steps of problem definition, decision-making, and objective confirmation; this is likely to be a crucial factor in determining the results of the experiment. Furthermore, by analyzing the performance of the experimental group in further detail, we found that the experimental group was better at estimating the influence of each factor during the modeling process and thus produced the best solutions; members of the experimental group were subsequently able to confirm whether a solution met the criteria set by the problem definition and review the general applicability of their solutions. This result is consistent with the findings of Atman et al. ( 2007 ), who found that the experimental group was able to determine the relevant causal relationships of a problem and discover the most important factors for solving the problem; on this basis, they identified the focal points of the problem, performed feasibility analyses, reviewed the constraints, and determination criteria of the problem, and provided simple explanations of the results that could be produced by further analysis. Therefore, it is our opinion that teaching goals should be cross-evaluated and explained in advance; additionally, more time should be dedicated to problem scoping and the development of alternative solutions. Furthermore, more effort should be dedicated to feasibility analysis to shorten the time needed by preservice technology teachers to solve engineering problems. On this basis, we expect the incorporation of engineering design process pedagogy to strongly improve teaching effectiveness.

Prioritize procedural learning in modeling and redesign for preservice technology teachers in STEM project-based learning

This approach can improve preservice technology teachers’ capacity for sophisticated thinking. Hynes ( 2012 ) noted that the prototype construction and redesign steps of the engineering design process affect the development of sophisticated thinking. Our results showed that the preservice technology teachers who received engineering design process pedagogy outperformed the control group in the prototype construction and redesign steps. Wu and Tsai ( 2005 ) proposed that constructivist science activities could provide opportunities for “cognitive apprenticeship,” as these activities allow for the application of metacognition and high-level information-processing strategies to the organization of cognitive structures. Furthermore, during these learning activities, students are provided with opportunities for expression, communication, and consultation, which improve their cognitive structures in engineering design thinking. Therefore, we suggest that the engineering design process be incorporated through engineering-related, project-based learning activities. This will help preservice technology teachers formulate improved designs using their engineering design knowledge and improve their cognitive structures in engineering design thinking.

In this study, the flow-map method was used to acquire and analyze the cognitive structures of preservice technology teachers; quantitative and qualitative analyses were also performed on the collected data. We found that the preservice technology teachers who were taught the engineering design process significantly improved their performance in prototype construction and redesign. Similarly, Atman et al. ( 2007 ) found that expert engineers are skilled at making decisions about engineering-related concepts because they understand a broad range of engineering concepts, and their experience enables them to easily and accurately evaluate various aspects of the engineering design process (e.g., problem descriptions, prototypes, work plans, independent completion time). Successful professional engineers are highly adept at applying their metacognitive skills and understanding of engineering design thinking concepts. We believe that incorporating the engineering design process into the training of preservice technology teachers is beneficial for refining their cognitive structure in engineering design thinking. However, the design of these teaching courses should be based on the progress of preservice technology teachers in engineering-related courses to improve their capacity for sophisticated thinking.

Suggestions for future research

One of the main advantages of engineering design process pedagogy is that it cultivates sophisticated thinking. However, there are many difficulties in implementing this mode of pedagogy (Linder, 1999 ). Many teachers feel that the obstacles to its implementation are too severe, and the issue most frequently cited is insufficient time. If the weaknesses of preservice technology teachers can be discovered through preclass investigation, and a few elements of engineering design thinking pedagogy can be added to address these weaknesses, this approach will undoubtedly enhance teaching effectiveness without wasting too much time on trial and error. Furthermore, as EDP-STEM-PBL involves several skills that are generally applicable to other tasks (Atman et al., 2005 ), improving the problem scoping and development capabilities of preservice technology teachers will also improve their ability to devise alternative solutions. Engineering design activities prepare preservice technology teachers for realistic problems, provide opportunities for practice, and improve their abilities in various areas, including their ability to integrate knowledge. Therefore, designing learning activities that are relevant to real-life will cultivate practical knowledge and problem-solving capabilities in preservice technology teachers and improve their ability to address real-life subjects.

Availability of data and materials

The quantitative data and materials are in Chinese. If you need the data and materials, please contact the corresponding author.

Abbreviations

STEM project-based learning combined with the engineering design process

National Taiwan Normal University

STEM-PBL curriculum based on the technological problem-solving process

Preservice technology teacher

Science, technology, engineering, and mathematics

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Acknowledgements

We are extremely grateful to the teachers and students who participated in this study.

This research was funded by the Ministry of Science and Technology of the Republic of China under Contract numbers MOST 105-2628-S-003-001-MY3, MOST 108-2511-H-003-058-MY4 and the “Institute for Research Excellence in Learning Sciences” of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. The findings and recommendations contained in this article of those of the authors and do not necessarily reflect those of the Ministry of Science and Technology.

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Kuen-Yi Lin is the leader of this research, he is in charge of the research design, conducting teaching experiment, data analysis, and writing the manuscript. Ying-Tien Wu is responsible for analyzing the flow map and qualitative data with Dr. Lin. They spent more than 3 months to discuss and analyze the data. Besides, they also discuss the possible reasons in explaining the research results. Yi-Ting Hsu is responsible for collecting and analyzing the related literature, drawing the flow map according the qualitative data of in-depth interview and metalistening. P. John Williams is responsible for providing comments to this research, and he is also responsible for revising the manuscript due to his first language is English. The author(s) read and approved the final manuscript.

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Lin, KY., Wu, YT., Hsu, YT. et al. Effects of infusing the engineering design process into STEM project-based learning to develop preservice technology teachers’ engineering design thinking. IJ STEM Ed 8 , 1 (2021). https://doi.org/10.1186/s40594-020-00258-9

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The anatomy of a component sprint

The Washington Post’s inclusive process for creating new design system components bridges the gap between design and development to make features that help navigate the news online.

At The Washington Post, we help readers understand the rapidly evolving events shaping their world. In order to deliver the news quickly, reliably, and at a high quality, we need tools and processes that accelerate collaboration. So, in 2019, we embarked on an ambitious journey to craft The Washington Post Design System (WPDS) .

Building a rich library of interactive components hasn’t been easy. In the early days of the design system, we shipped new components either through a design-led approach (as in the case of select , radio and checkbox inputs), or a developer-led approach when it involved more technical complexity (as in carousel and input search ). Both of these scenarios had their pitfalls, causing delays and unexpected compromises. It became clear that we needed to get the right people in the room early on to offer guidance and close knowledge gaps. After years of refinement, we’ve developed a component sprint that does just that. Led by a designer-developer pair, the component sprint acts as a bridge between teams and ensures meaningful collaboration at every step.

A vertical illustration shows a 10-day overview of a team building a boat. Day 1, Kickoff, shows people talking in a shipyard. During Day 2, Concept, they gather around a table and test model boats in a pool. On Days 3 to 6, Design and Implementation, one side sketches designs while the other sews and constructs parts of the boat. Days 6 to 7 are for Refinement, and the workers clean and paint the boat. Day 8 to 10, Documentation, shows sailors on the boat while people on the dock take photographs.

If you’re reading this, you’re probably familiar with the pains of siloed design and development workflows. Before we instated the component sprint, we suffered mismatched expectations around how a component should look or function. At times, developers were put in the awkward position of pointing out potential technical limitations, or being pressured to focus on nuanced visual details rather than devising solutions. At other times, miscommunication limited the contexts in which a new component could be deployed. Designers were disappointed that their use cases weren’t fully considered—or worse, were unaware of existing components that could meet their needs. As a result, some opted for their own solutions, which led to frequent overrides and requests for features and variants that needed to be assessed on the fly by the design system team.

The component sprint evolved our process from a closed, linear approach to a more open and inclusive one. The roughly 10-day process guarantees balanced, democratic input, and we kick one off for every single component we plan to work on, though some sprints go faster than others. Here’s how it works.

The component sprint evolved our process from a closed, linear approach to a more open and inclusive one. “ The component sprint evolved our process from a closed, linear approach to a more open and inclusive one. ”

Kickoff: Charting impact versus effort

To have a true sense of shared stewardship in what is being built and shipped, gathering as much input as possible is crucial. For example, we have a weekly 30-minute meeting where we evaluate our candidate component tickets in Jira based on potential impact versus effort. Each ticket on our idea board is a dynamic space for stakeholders to leave feedback and insights asynchronously. We also use these tickets to capture relevant information from our many conversations on Slack.

A screenshot shows circles of different sizes and colors, which represent popular ideas and the business goals they’re aligned with, charted on an impact-versus-effort matrix.

Once we prioritize a component ticket based on everyone’s input, we assign a designer and developer from the core design system team to take charge of its delivery. Pairing them from the start ensures that communication happens throughout the entire process.

Concept: Defining scope and goals

Next, we schedule a two-hour meeting to kick off a sprint with the wider team. In a FigJam file, we define goals, agree on requirements, assess the scope of work, and leave 15 minutes for review. Gathering everyone in a FigJam file helps further democratize the design process because it allows both technical and non-technical contributors to clarify expectations, align on assumptions, and agree on technical requirements for the component without getting mired in design specifics just yet.

A screenshot of a FigJam file shows clusters of stickies next to considerations like form factor, positioning, viewport / display behaviors for narrow widths, and relationship to similar components.

After aligning on a shared vision, we move into a state of mutual discovery. During this hour, we drop screenshots, links, reference code snippets, and other artifacts from our site and around the web into the FigJam, annotating them according to our defined toolbox: API, behavior, accessibility, animation, options or variants, considerations, and requirements. This framed approach allows us to quickly capture and categorize insights from everyone in the meeting while narrowing the findings according to our scope and goals.

The image displays a selection of colorful labels: API, Accessibility, Requirement, Behavior, Option, Animation, and Consideration. A dark widget at the bottom suggests a space for adding code snippets.

For example, as we explored the action menu component, the exercise revealed a range of expectations around how it would function. Some stakeholders felt it would facilitate navigation, citing examples of navigational menus, but we’d already designed another component for that purpose. This prompted a thorough discussion. To avoid confusion and more accurately reflect its intent, we transitioned from calling it a “dropdown” to an “action menu.”

A blue gradient blob featues text that reads “Replace with Component: Component Sprint.” The background is light blue with scattered blue geometric shapes.

Run your own component sprint with this template modeled after The Washington Post’s workflow.

Design, implementation, and refinement

As the sprint continues, the funnel for participation narrows. After the first component sprint meeting, the designer-developer pair that we initially identified work asynchronously, touching base often to make sure that technical constraints inform the concept, and that the visual design informs the implementation. At this stage, both the developer and designer understand their work is malleable: It can be reworked, or even discarded, and no one is the villain for suggesting a change. Continual dialogue is the bedrock of this phase, ensuring that both the design and technical aspects evolve cohesively.

Both the developer and designer understand their work is malleable: It can be reworked, or even discarded, and no one is the villain for suggesting a change. “ Both the developer and designer understand their work is malleable: It can be reworked, or even discarded, and no one is the villain for suggesting a change. ”

Side-by-side comparison of an early stage design of the input search component, with the left side displaying the Figma mock-up and the right side showing the corresponding coded component.

During the development of the input search component, for instance, a key point of discussion was the component’s empty state. The designer envisioned dynamic empty states reacting to user input: an initial state with a generic message, an active state reading “searching for…,” and a no-result state with a message like “no results found.” The developer, on the other hand, prepared for a static empty state based on actual search results. Constructive dialogue helped them reach a consensus to implement a default empty state that users could customize to be dynamic. This then simplified the workload for both teams and created flexibility for end users.

When it comes to the final product, all of our components are delivered in the Figma and code libraries at the same time, so no one is designing or building with assets that don’t exist. The designer-developer pair coordinates to make sure these changes happen in sync.

Delivering preliminary documentation

Documentation isn’t just an outcome of the sprint—it’s an integral part of our process. By documenting our insights, decisions, and plans on the site , we ensure the longevity and adaptability of our components. It serves multiple roles:

Blueprint for implementation: a shared point of reference for designers and developers.
Record of decisions: the rationale behind decisions made during the sprint. This transparency is invaluable when new team members join or when we revisit a component to make informed modifications or extensions.
Stakeholder communication: a touchstone for communication with wider stakeholders and a way to invite feedback early in the process.
Feedback loop: a tangible artifact that facilitates further iterations, as the component sprint might reveal additional considerations or optimizations.
Knowledge transfer: an environment where anyone in the organization can understand the intent, capabilities, and limitations of a component, promoting informed usage.

Title card reading “#WPDS UI Kit.” To the right, an isometric illustration of three-dimensional toy building blocks with two leaves attached. Background is gray scattered with various geometric shapes.

The Washington Post Design System UI kit documents all their components.

Since adopting the component sprint, we’ve cut down the delivery time of a new component from six weeks to approximately 10 days. The process has greatly minimized, and in some cases completely eradicated, blockages due to technical limitations or the need for last-minute compromises. While we track design system adoption through internal tools and Figma’s library analytics , it’s ultimately part of our culture—and considered in our annual performance goals—to exhaust the design system as a resource in both design and development.

Design systems don’t just deliver components—they act as bridges, facilitating connection and understanding across teams. The true power of a design system lies in bringing together diverse perspectives to achieve collective goals, which is why an open process is so important; when everyone has a stake in the system, the result is shared stewardship. The evolution of our component sprint ensures that each component is robust, adaptable—and mostly importantly—meaningful to its users. We hope our journey inspires others to continually reimagine their own processes.

Brian Alfaro seamlessly blends creativity, technology, and strategy to shape the growing system of components and foundations to help bring news and stories to all. Off-duty, he is an avid cartoon enthusiast, game dev, mountain biker, and nature photographer. Super proud to be in the 3D club (designer, developer, and dad).

Victor is a designer, artist, and nerd based in Philadelphia. He is a former Senior Product Designer at The Washington Post where he focused on front-end design for editorial content, audio and video experiences, and design system development and technical writing. He spends most of his day thinking about communication, interfaces, aesthetics, and memes.

Illustrations by Peter Gamlen

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MIGNAGOLA DI CARBONERA (TV)

PROCESS ENGINEERING SPECIALIST (M/F/X)

Job responsibilities.

Purpose: Study, design, set up and implement all the processes workflow . Try to optimize the processes and working times, as well as the optimal conditions of use of the production lines and resources. Support other Company Plants to implement new solutions or new projects

Principle Accountabilities & Responsibilities: ? Develop, analyze and enhance manufacturing methods to assist production requirements ? Develop the investment plan for equipment, a preliminary mapping of the standard costs and shares the timing of advancement, development and production of Out Of Tools - First pre-series (OOT-PP) ? Set up the working times of the finished product production lines ? Analyze the work cycles of the various finished products (cycle times and operators) ? Design of work layouts and flow charts ? Insert the cycles in the SAP management system and keep them update ? Research improvement of the process/product, always having among the objectives the reduction of times and costs ? Analyze the root cause of problem, researching of new solutions ? Check the production FTQ daily, analyzing the trend of the data and highlighting any deviations or anomalies ? Advance purchase requests for the necessary equipment or collaborate with the tool shop (internal or external) for the design and construction of the equipment ? Collaborate with Purchasing Office regarding component, packaging.. costs ? Verify and take care of the assistance of equipment at subcontracting suppliers ? Collaborate with Technical Office/Quality in order to study and implement new solutions ? Collaborate for production budget definition

Job Profile

Qualifications & Knowledges: University Degree in engineering Previous experience of at least 5-10 years as Process Engineering Expert gained in medium-large companies operating Knowledge of the process, of the production cycles Knowledge of the Lean Production Model and Kaizen principles A good knowledge of English is mandatory.

Skills,capabilities & Competencies: Availability for business trips and transfers Flexibility Proactivity Accuracy Problem solving Team Working

Restiamo in contatto

Integrating the engineering design process into the conceive-design-implement-operate model for promoting high school students’ STEM competence

Development Article
Published: 15 April 2024

Cite this article

Feifei Xi 1 ,
Hongliang Ma ORCID: orcid.org/0000-0001-9177-8069 1 ,
Zhongling Pi 2 ,
Yuhan Dong 3 ,
Junmei Sun 1 &
Rucheng Jin 1

Recently, integrated science, technology, engineering, and mathematics (STEM) education has gained sustained attention in K-12 settings, and engineering design-based pedagogy has become a key issue. Compared with rich research in higher education, relatively few studies are performed on engineering education in K-12 schools. In this study, we combined Conceive-Design-Implement-Operate (CDIO) model with the engineering design process (EDP), naming EDP-CDIO, aiming to promote high school students’ STEM competence and compare its effects with the conventional CDIO approach. A pretest–posttest nonequivalent group design was conducted among 64 eleventh-grade students with eleven lessons. Quantitative data were collected via a pretest and posttest, and qualitative data were collected via artifacts and semistructured interviews. The repeated-measures analysis of variance and epistemic network analysis revealed that, compared with the conventional CDIO approach, the EDP-CDIO model significantly improved students’ STEM knowledge, skills, and attitudes and developed more comprehensive epistemic networks in STEM competence. These findings provide a reference for K-12 STEM teachers, encouraging them to implement the EDP-CDIO model more frequently in the classroom, especially with the iterative design process.

Avoid common mistakes on your manuscript.

Introduction

STEM education has attracted worldwide attention (English, 2017 ; Takeuchi et al., 2020 ). The STEM movement drives students to pursue careers in STEM fields, helps improve their learning achievement, and helps them develop 21st-century skills. However, the concept of STEM itself and how it should be integrated is still open to debate (Bybee, 2013 ; English & King, 2019 ). English ( 2017 ) suggests that STEM education involves one of the four STEM-related domains or interrelated curriculum. Compared with an individual STEM curriculum, integrated STEM removes barriers between interdisciplinary topics and emphasizes that students solve complex and real-world problems using knowledge from two or more of these disciplines (EL-Deghaidy et al., 2017 ; Gao et al., 2020 ; Kim et al., 2018 ). Nevertheless, engineering education has been severely neglected in K-12 STEM education (English, 2017 ; Strimel & Grubbs, 2016 ), resulting in the insufficient cultivation of engineering talent and leading to a critical engineer shortage in many countries (Han et al., 2015 ; Nehdi, 2002 ).

In recent years, there has been a consensus among researchers that engineering education promotes the advance of STEM education. Engineering education creates real-world environments, provides important foundational links across STEM disciplines, and improves students’ problem-solving, communication, and cooperation skills (Brophy et al., 2008 ; English & King, 2017 ). In higher education, Conceive-Design-Implement-Operate (CDIO) model is a widely used framework for training the next generation of engineers. It emphasizes learning in the context of conceiving, designing, implementing, and operating real-world systems and products (Crawley et al., 2007 ). However, the CDIO model is few employed in K-12 education because school teachers find it challenging to implement teaching and learning activities in the classroom while utilizing the four-phase CDIO model. To address this issue, we integrated the engineering design process (EDP), which emphasis on iterations to find a problem-solving solution under specific constraints (Kelly & Sung, 2022 ), into the conventional CDIO model in an engineering course for high school students, and then examined its effect on students’ STEM competence further.

Literature review

Cdio in stem education.

CDIO is an international initiative to reform engineering education started by MIT with three universities in 2000 (Crawley et al., 2007 ), and emphasizes the foundations of Conceiving-Designing-Implementing-Operating products, processes, and systems (Crawley et al., 2008 ). The Conceive stage involves identifying client needs, considering technology, and developing conceptual and technical plans. The Design stage is concerned with creating the design and describing the product to be implemented. The Implement stage focuses on how the design is turned into the final product, including manufacturing, coding, and testing. The Operate stage involves using the implemented product to produce the desired value, including maintaining, evolving, and recycling the product (Crawley et al., 2007 , 2008 ). Currently, more than 200 universities worldwide have joined the CDIO initiative, which has become an advanced engineering education model in North America, Europe and Asia (Wang et al., 2021 ). As an innovative educational framework, the CDIO model addresses the imbalance in engineering education by advocating for less emphasis on theory and a greater focus on practical application (Chen et al., 2020 ). It proposes to create a more hands-on and project-based learning experience that better strengthens students’ practical ability and skills, improves students’ engineering awareness, and prepares students for the multidisciplinary and collaborative nature of today’s engineering challenges (Wang et al., 2021 ; Zhong et al., 2021 ).

The CDIO model has been used in different subject areas in higher education, such as computer science (Lai et al., 2021 , 2022 ; Song et al., 2017 ), chemistry (Hu & Li, 2020 ; Wang et al., 2021 ), aeronautics and astronautics (Crawley et al., 2008 ; Padfield, 2006 ). Many researchers have employed the CDIO model to change how students learn and teachers teach. They found that it improved students’ domain knowledge (Jambari et al., 2019 ; Lai et al., 2022 ), cognitive skills (e.g., problem-solving, creativity, and critical thinking), cooperation and communication skills (Haupt, 2018 ; Leslie et al., 2021 ; Taajamaa et al., 2016 ), and learning interest (Wang et al., 2021 ). For instance, Zhong et al. ( 2022 ) applied the CDIO engineering design framework to a STEM course using a pretest–posttest nonequivalent-groups design. The results confirmed that the course significantly improved undergraduate students’ creativity.

However, there is limited research on implementing CDIO engineering design in K-12 integrated STEM courses. To our knowledge, only the study of the Ma et al. ( 2020 ) tried to develop a new inquiry learning framework that combines the LEGO 4Cs process (Connect, Construct, Contemplate, Continue) and the CDIO model in a primary school robotics course. The results showed that the framework significantly improved elementary students’ STEM engagement and engineering thinking skills. Nevertheless, that framework was only conceptual and did not describe how the CDIO model was specifically integrated into the robotics course. In the present study, we identified the specific processes through which the CDIO engineering design framework was applied to integrated STEM learning activities and adopted a multilevel evaluation approach to examine the effectiveness of the CDIO model in a high school.

Integrating the engineering design process into the CDIO model

Although CDIO is considered an innovative framework for engineering education reform (Crawley et al., 2007 ), it is not easy for teachers to enact teaching and learning activities in the classroom when using the four-phase model of CDIO in K-12 education (Ma et al., 2020 ). Some researchers attempted to extend the CDIO model from different perspectives. For example, Taajamaa et al. ( 2016 ) integrated a human-centered design thinking approach into the CDIO lifecycle, adding “Observe” phase to develop an O-CDIO model, which emphasizes the importance of problem identification. Lai et al. ( 2021 ) created a Design Thinking (DT)-CDIO engineering design framework in a flipped web programming course to facilitate students’ interdisciplinary learning, which includes analyzing, observing, reflecting, conceiving, designing, implementing, and operating activities. According to previous studies (Crawley et al., 2007 ; Lai et al., 2021 ; Taajamaa et al., 2016 ), it has been found that while the CDIO model is effective in providing guidance for problem-solving, it may not offer adequate support for identifying and comprehending problems, especially referring to the actual implementation of the CDIO model in practice. In addition, many studies have pointed out that the engineering design process (EDP) emphasizes the identification of constraints to identify problems, considering multiple possible solutions, and emphasizing optimization after testing the initial solution (Crismond & Adams, 2012 ; English et al., 2017 ; Hynes, 2012 ). EDP makes up for the ambiguity of problem definition in the CDIO model and enhances the operability of implementation in K-12 STEM education. Therefore, incorporating the EDP into the existing CDIO model and extending the EDP-CDIO model into activity design can serve as an alternative and innovative pedagogy.

The previous literature described the engineering design process in different forms with various open-ended and iterative activities. For instance, Hynes ( 2012 ) suggested eight engineering design activities: identifying needs or problems, researching those needs or problems, developing possible solutions, selecting the best possible solution, constructing a prototype, testing and evaluating solutions, communicating solutions, and redesigning them. English et al. ( 2017 ) developed a model of the engineering design process including five stages: problem scoping, idea creation, designing and constructing, design assessment, and redesign and reconstructing. In present study, based on the CDIO model and the engineering design process (English et al., 2017 ; Hynes, 2012 ), an adapted CDIO model named EDP-CDIO was developed to facilitate teachers’ teaching practice, especially learning and teaching activities in the classroom.

STEM competence and its assessments

Competence consists of knowledge, skills, and attitudes that can be used to perform a professional task (Baartman & de Bruijn, 2011 ; Council of the European Union, 2006 ). In this study, STEM competence refers to conceptual knowledge, practical skills and attitudes that students need to engage in STEM-related activities (Bybee, 2008 ; Hu et al., 2020 ). As the demand for and interest in a STEM-skilled workforce increases, more interventions are aimed at developing students’ STEM competence.

In recent years, there has been growing interest in how to assess students’ STEM competence. Regarding STEM knowledge, tests are widely used for this task (Chiang et al., 2022 ; Fan & Yu, 2017 ). For example, Han et al. ( 2021 ) used the D-BAIT knowledge test developed by the TRAILS research team to evaluate students’ STEM knowledge in three subject areas: engineering design, physics, and biology. Furthermore, Kelley et al. ( 2022 ) developed a test instrument consisting of 20 multiple-choice items to measure knowledge in integrated STEM fields, including biomimicry, engineering design, physics, and biology. Regarding STEM skills, researchers generally evaluate procedural and cognitive skills, such as problem solving, critical thinking, creativity, collaboration, etc. (Chen et al., 2023 ; Jang, 2016 ). Researchers often adopt semistructured interviews (Falloon et al., 2022 ), scale surveys (Gönen & Korkmaz, 2022 ) and artifact assessments (Hu et al., 2020 ). Regarding STEM attitudes, self-reported scales have been developed (Mahonry, 2010 ; Unfried et al., 2015 ). For example, Guzey et al. ( 2014 ) developed a scale for students’ attitudes toward STEM that includes three dimensions: STEM learning, STEM employment, and STEM social impact. Benek and Akcay ( 2019 ) evaluated students’ attitudes toward STEM from six dimensions: science, mathematics, technology, engineering, interdisciplinary, and career topics.

However, previous studies have mainly focused on evaluating students’ STEM knowledge, STEM skills, or STEM attitudes separately (Ching et al., 2019 ; Han et al., 2021 ; Kelley et al., 2022 ; Sisman et al., 2021 ). Few studies have evaluated students’ STEM competence from a holistic view (Hu et al., 2020 ).

Purpose and research questions

As described in the literature review above, previous studies (e.g., Lai et al., 2021 ; Taajamaa et al., 2016 ) have attempted to extend the CDIO model in higher education from different perspectives. However, few studies tried to integrate EDP into the CDIO model and examine the potential impact of the EDP-CDIO model on high school students’ STEM competence. Additionally, it may be beneficial to include a comprehensive evaluation to measure students’ knowledge, skills, and attitudes towards STEM. The purpose of this study is to investigate the effects of the EDP-CDIO model compared to the conventional CDIO approach on the STEM competence of high school students. Consequently, the main research questions (RQs) are specifically proposed as follows:

Which type of STEM activity (EDP-CDIO or conventional CDIO) is more effective in improving high school students’ STEM knowledge and attitudes?

How do EDP-CDIO activities affect students’ STEM skills compared to conventional CDIO activities?

What are the differences in the epistemic network characteristics of students’ STEM competence in EDP-CDIO activities versus those of conventional CDIO activities?

Theoretical framework

This study develops the EDP-CDIO engineering design model with specific eight learning procedures so as to facilitate STEM teaching and learning in the classroom, as shown in Fig. 1 .

EDP-CDIO framework

Identify (identify the problem)

Students need to identify the root cause of the real-world problem, develop a detailed problem statement, determine design objectives, and define task constraints (English & King, 2015 , 2019 ).

Search (search and analyze information)

There are some steps and strategies to effectively search for and analyze information, including: define the scope, select appropriate sources, read critically, compare and contrast, and synthesize information, etc. (Atman, 2019 ; Childress & Maurizio, 2007 ).

Brainstorm (brainstorm and share ideas)

Brainstorming is a creative process used to generate a broad range of ideas for solving a problem. When brainstorming and sharing in a group, students need to use diverse brainstorming techniques, capture all ideas on a whiteboard, and present ideas clearly, etc.

Design (design possible solutions)

Students are required to individually take the selected ideas and flesh them out into more detailed concepts. They then create possible plans, sketches, models, or prototypes of the concepts within a group.

Select (select an optimal solution)

Students are encouraged to analyze, evaluate and reflect on the designed solutions. They then select an optimal one in groups based on criteria such as feasibility and alignment with goals and constraints.

Implement (build and test the product)

This phase involves tasks such as cutting materials to size, assembling components, wiring electronics, coding software, or printing 3D objects. Once the physical model is built, perform initial and structured tests, and then analyze the testing data (Crawley et al., 2007 ).

Redesign & reimplement (redesign, rebuild, and retest)

This may involve using the insights gained from testing to refine the model, making changes (English & King, 2015 ; Hynes, 2012 ), redeveloping the product, and retesting it to evaluate if the adjustments have had the desired effect and the revised product performs adequately.

Operate (communicate and share the results)

Effective communication of the final results is about telling the story of the project, including the challenges faced, the solutions developed, and the knowledge gained. This phase involves drafting a report, developing a presentation, sharing your work, soliciting feedback and follow-up.

Compared with the conventional CDIO model, this alternative framework has potential learning advantages, including: (1) Requiring students to clarify the goal of the problem, recognize constraints, and pinpoint the core issue, which is critical for developing effective and viable solutions. (2) Encouraging students to engage in brainstorming and group sharing, which can leverage the diverse experiences and perspectives of the group and lead to more innovative solutions. (3) Highlighting the importance of an iterative and reflective learning process, which allows students to critically assess their work and learn from both their successes and mistakes in real-world engineering practices, where continuous improvement is often necessary to achieve the best possible outcomes.

Participants

This present study was conducted in a public high school that is situated in a socioeconomically diverse area located in a developing city in southwest China. The school caters to a large number of students, with an enrollment of more than 3,000 students from grades 10 to 12. A total of 64 students from two regular 11th grade classes participated in this study voluntarily, with ages ranged from 16 to 17 years old. In this study, The EDP-CDIO and conventional CDIO groups were assigned randomly. The former group consisted of 13 boys and 19 girls, and the latter consisted of 12 boys and 20 girls. The demographic information is shown in Table 1 . Before the intervention, these students had little integrated STEM learning experience, and the prior STEM knowledge and STEM attitude surveys showed no significant difference between the two groups (respectively, p STEM knowledge = 0.917 > 0.05; p STEM attitude = 0.766 > 0.05). In addition, all students in both classes were randomly divided into eight teams, with four students on each team. This study was approved by the local ethics committee, and consent was obtained from all participants’ parents.

Research design

As shown in Fig. 2 , this study adopted a pre- and posttest quasi experimental design with experimental and control groups. In the experimental group, the EDP-CDIO model was implemented to organize learning activities, while the conventional CDIO model was implemented in the control group. The whole experiment lasted for 6 weeks, with two 45-min lessons per week. Pretests of STEM attitudes and knowledge were conducted in both groups in the first lesson. Posttests were administered to test the STEM attitudes and knowledge of the two groups in the last lesson. Furthermore, after the intervention, a total of 16 students were selected for the semistructured interviews, with eight students (5 girls and 3 boys) from the experimental group and eight (5 girls and 3 boys) from the control group. The selection process aimed to ensure a balanced and representative sample by choosing randomly choosing one member from each of the learning teams within the classes. Students’ artifacts were also collected during the intervention. Additionally, a teacher with 10 years of teaching experience (one of the researchers) taught the two classes with the same STEM project and materials, both supported by the same teaching assistant (responsible for the classroom management).

Intervention

Content design.

In this study, an integrated STEM project named “Creative Sky Ladder” was adapted from a unit of the Technology and Design course. Two researchers and three high school teachers collaboratively designed and developed the learning activities for the two classes. In the STEM project, students were required to design and build a ladder structure, simulating carrying a large snowmaking machine from the bottom to the top of a mountain for the Olympic Winter Games Beijing 2022 alpine skiing event. Materials used to make the ladder structure include wood sticks (5 × 5 × 250 mm), glue guns, rubber bands, cotton spools, scissors, and paper cutters. Students also needed to design and print a 3D gear using PLA (polylactic acid) filament, and then code Arduino to control a TT motor that drives the 3D gear and lifts the weights, as shown in Figs. 3 and 4 . In addition, the task constraints were that the ladder’s height should be more than 700 mm, the net weight of the ladder should be less than 230 g, and the minimum load capacity should be 2000 g. Furthermore, each team was required to construct the ladder structure within 90 min. Table 2 shows the integrated concepts and connotations of STEM in the project.

STEM project. a The materials used and the ladder built in the EDP-CDIO group, b building the ladder in the conventional CDIO group, c testing the ladder in the EDP-CDIO group

Interactive design process in the EDP-CDIO group. a the first-round design of ladder structure, b the second-round design of ladder structure in the same team; c the first-round design of 3D gears, d the second-round design of 3D gears in the same team; e Codes in Arduino C program for voice-controlled motor rotation

Activity design

In the STEM activities, as shown in Table 3 , students in the EDP-CDIO group followed the steps of identifying, searching, brainstorming, designing, selecting, implementing, redesigning and reimplementing, and operating. Students in the conventional CDIO group designed and built the ladder by following the steps of conceiving, designing, implementing, operating (Crawley et al., 2007 ). The learning activities for both groups were conducted by the same teacher and teaching assistant, with the teacher also serving as a member of the research team and possessing a thorough understanding of the two types of CDIO learning procedures. Prior to the intervention, an extensive discussion took place among the researchers to ensure a unified approach. During the intervention, the teacher and assistant maintained close collaboration and regular communication, overseeing the conditions under which the learning activities were carried out for both classes. This included ensuring uniformity in learning resources, hands-on materials, and the allotment of learning time across the two groups, except for learning activities and procedures.

Instruments

Stem knowledge tests.

To examine students’ STEM knowledge, this study revised a province-level STEM knowledge test, the Academic Proficiency Test of General Technology in Guizhou Province, into two homogeneous pre- and posttest versions. To ensure content validity, the two versions of the knowledge tests were adapted by an expert team, including three Ph.D. students with STEM education backgrounds and six high school teachers with 7–8 years of teaching experience in STEM education. Both the pre- and posttest consisted of 20 single-answer multiple-choice questions to examine various integrated STEM knowledge (e.g., SE, TM, STE, SME, etc.). Each question had four choices, with 3 points given for the correct answer and 0 given for the wrong answer. Some examples of the pre- and posttest items are shown in Table 4 . The Cronbach’s alpha of the pretest and posttest of STEM knowledge were 0.72 and 0.78, respectively. It showed acceptable reliability in internal consistency. Between the two parallel forms of the tests, the Spearman-Brown coefficient was 0.78 ( p < 0.001), which confirmed the parallel form’s reliability of the pre- and posttest.

STEM attitude scale

In this study, the STEM attitude scale was adopted from Benek and Akcay ( 2019 ) and translated into Chinese. This scale consists of six subdimensions: science (four items), mathematics (eight items), engineering (six items), technology (six items), interdisciplinary (five items), and career (four items). Students rated all items on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The Cronbach’s alpha of the Chinese version was 0.95, indicating that the scale had high reliability.

Evaluation criteria for STEM artifacts

To assess students’ STEM skills, this study adopted artifact assessments approach (Hu et al., 2020 ). The criterion was modified from a nationally approved textbook (General Technology Textbook Writing Group, 2016 ) to evaluate 7 dimensions of students’ ladder artifacts, including height, net weight, load capacity, structural stability, structural strength, aesthetics, and maximum lifting height of loads. The total score was 28 points, and the score of each dimension ranged from 1 (poor) to 4 (excellent). According to the revision of Bloom’s taxonomy (Anderson & Krathwohl, 2001 ), making a STEM artifact involves students in higher levels of learning (analyzing, evaluating, and creating) which reflects student-centered hands-on activities (Hu et al., 2020 ).

Semistructured interview outline

To answer RQ2 and RQ3, the semistructured interview focused on the impact of the STEM activities on students, including the following: (1) What did you gain and what are your feelings in the STEM activities? (2) What challenges did you encounter? How did you solve them? (3) Do you think these activities are helpful for your STEM knowledge/skills/attitudes? If yes, please provide some specific examples. (4) Do you think these activities will influence your future STEM career choices? If so, why?

Data collection and analysis

To answer RQ1, STEM knowledge tests and STEM attitude scales were distributed through online questionnaires. Then, valid responses were screened using the pairwise deletion method (Graham, 2009 ). After eliminating invalid responses, 61 valid responses remained with a valid rate of 95%. The result of the Shapiro‒Wilk test showed that the pre- and posttest scores of STEM knowledge ( p > 0.05) and STEM attitudes ( p > 0.05) were normally distributed. Therefore, a repeated-measures analysis of variance (ANOVA) was employed to analyze the data further.

To answer RQ2, eight ladder artifacts from eight teams were collected from each class, and then were assessed independently by two researchers according to the Evaluation Criteria for STEM Artifacts. The inter-rater reliability of the two researchers was evaluated using intra-class correlation coefficient (ICC). The average ICC measure for each dimension was more than 0.76, indicating good reliability (Koo & Li, 2016 ). During the evaluation, the two researchers discussed the differences together and reached a consensus. Due to the small sample size ( n = 8) in each class, the Mann‒Whitney U test was selected and its use was justified to analyze the score of the artifacts. Furthermore, researchers conducted semi-structured interviews with a total of 16 participants from EDP-CDIO and conventional CDIO groups to collect qualitative data. Each participant was interviewed for 15 to 20 min. With participants’ permission, interviews were audio-recorded and transcribed into text for further analysis. For the coding scheme, Jang’s ( 2016 ) STEM competence framework was adapted to analyze the interview data (see Table 5 ). The first dimension (STEM knowledge) of the scheme refers to science, technology, engineering, mathematics, and interdisciplinary knowledge; the second dimension (STEM skills) refers to critical thinking, creativity, problem-solving, cooperation and communication; and the third dimension (STEM attitudes) focuses on attitudes toward subjects and careers. For RQ2, frequency counts of STEM skills mentioned by the interviewees were coded according to the second dimension of the scheme.

To answer RQ3, epistemic network analysis (ENA; Shaffer, 2017 ; Shaffer et al., 2016 ; Shaffer & Ruis, 2017 ) was employed based on the semi-structured interviews among the 16 students. The STEM competence framework outlined in Table 5 was also adopted to conduct ENA. Each sentence with complete semantics of the interview data was used as a coded line, and 233 coded lines were obtained. Each coded line could contain one or more codes. Initially, two researchers independently checked a set of 99 coded lines to test the coding consistency. The Cohen’s Kappa value for each trial code was above 0.77, demonstrating good reliability (Cohen, 1988 ). Then, all the coded lines left were randomly split into two halves and were coded by the two researchers separately. Differences in coding were resolved through discussion. All analyses were performed using the ENA web tool ( http://app.epistemicnetwork.org ).

RQ 1: improvement of STEM knowledge and attitudes

A repeated-measures ANOVA was conducted to investigate differences in students’ STEM knowledge across the two learning strategies, with time (pretest, posttest) as the within-subjects factor and learning strategy (EDP-CDIO model, conventional CDIO approach) as the between-subjects factor. The descriptive and ANOVA results are presented in Table 6 . The results showed the significant main effects of learning strategy and time on students’ STEM knowledge. In addition, the interaction effect between Learning Strategy and Time was significant. Simple effects analyses found that students in the EDP-CDIO group showed significantly higher STEM knowledge scores than those in the conventional CDIO group in the posttest ( MD = 6.02, p < 0.001, see Fig. 5 ) but no significant difference in the pretest ( MD = 0.21, p = 0.917).

STEM knowledge in pre- and posttest across the two learning strategies (*** p < .001)

To examine the differences in students’ attitudes toward STEM across the two learning strategies, a series of repeated-measures ANOVAs was conducted on each of the six subdimensions of STEM attitudes, with time (pretest, posttest) as the within-subjects factor and learning strategy (EDP-CDIO model, conventional CDIO approach) as the between-subjects factor. Descriptive and ANOVA results for each subdimension are shown in Table 7 . This result indicated significant main effects of learning strategy and time on students’ attitudes toward STEM. More importantly, the interaction effect was significant. Simple effects analyses found that students in the EDP-CDIO group showed significantly higher attitudes toward STEM than those in the conventional CDIO group in the posttest ( MD = 0.50, p < 0.001, see Fig. 6 ), but there was no significant difference in the pretest ( MD = 0.04, p = 0.766). Furthermore, regarding the six sub-dimensions, only “interdisciplinary” showed a non-significant main effect of learning strategy and interaction effect of learning strategy and time, but with a significant main effect of time. This result implied that students’ attitudes toward “interdisciplinary” in both groups have improved, but there was no significant difference in the improvement between the two groups.

STEM attitudes pre- and posttest across the two learning strategies (*** p < .001)

RQ 2: influence on STEM skills

A Mann‒Whitney U test indicated that the artifact scores of the EDP-CDIO group (Mdn = 21.50, Mean rank = 11.94, N = 8) were significantly higher than those of the conventional CDIO group (Mdn = 16.50, Mean rank = 5.06, N = 8), U = 4.50, Z = − 2.907, p = 0.004, r = 0.70, indicating a large effect size, as shown in Table 8 . The result indicated that student’s STEM skills in the experimental group were significantly higher than that in the control group.

For the interviews, five themes related to STEM skills were identified in the EDP-CDIO and conventional CDIO groups. The frequency counts of those themes are displayed in Table 9 . Most participants in the EDP-CDIO group mentioned that their STEM skills were improved positively. Meanwhile, all the eight interviewees in the EDP-CDIO group expressed positive views about “problem-solving” with 37 times, “critical thinking” 32 times, “cooperation” 27 times, “communication” 14 times, and “creativity” 7 times, which were all higher than the frequency counts of those themes in the conventional CDIO group. The result indicated that the participants in the EDP-CDIO group had higher STEM skills than those in the conventional CDIO group. In terms of problem-solving, participant G 2 in the EDP-CDIO group noted, “ In the process of building the ladder, interdisciplinary knowledge, such as physics, information technology, mathematics and other disciplines will be applied to help us analyze and solve the problem of the ladder bearing. ” Regarding critical thinking, participant B 1 in the EDP-CDIO group mentioned, “ After our team’s ladder structure collapsed at a load of 1400 g, we reflected on the issue. We identified the high center of gravity and weak connection between the wooden sticks as potential causes for the collapse. After another modification, our team finally completed the ladder with a maximum load of 3200 g. ” In terms of creativity, participant G 5 in the EDP-CDIO group said, “ In the stage of searching and analyzing information, I can find a lot of inspiration about the structure of the ladder, and through brainstorming, my thinking gets divergent. ”

RQ3: epistemic network characteristics of STEM competence

An ENA approach was conducted on the coded data to further explore the differences in the epistemic network of STEM competence between the EDP-CDIO and the conventional CDIO groups. As shown in Fig. 7 , each point is the centroid of a student’s epistemic network of STEM competence; the squares are the means of all group members’ centroids, and the boxes are the 95% confidence intervals. The first dimension (x-axis) accounted for 13.5% of the variance in the data, and the second dimension (y-axis) accounted for 15.0%. Furthermore, along the x-axis, a Mann‒Whitney test showed that the EDP-CDIO group (Mdn = 5.26, N = 8) was significantly different at the alpha = 0.05 level from the conventional CDIO group (Mdn = − 5.87, N = 8 U = 64.00, p = 0.00, r = − 1.00), and there was no significant difference along the y-axis, as shown in Table 10 . The Mann‒Whitney test and the epistemic network plot indicated significant differences in ENA characteristics between the EDP-CDIO group and the conventional CDIO group.

Students’ epistemic networks of STEM competence. The EDP-CDIO group is in red, and the conventional CDIO group is in blue (Color figure online)

To interpret the significant difference between these two groups, mean networks were plotted in the present study (see Fig. 8 ). The nodes in the figure, whose size was related to the frequency of occurrence of codes, correspond to the codes, and the thickness of the connection between nodes was correlated to the frequency of co-occurrence between the two codes. The ENA model had coregistration correlations of 1 (Pearson) and 1 (Spearman) for the first dimension and coregistration correlations of 1 (Pearson) and 1 (Spearman) for the second. These measures indicated a strong goodness of fit between the visualization and the original model.

Mean network and the difference network. a Mean network of the EDP-CDIO group in red, b Mean network of the conventional CDIO group in blue, c the difference network of the two groups (Color figure online)

Figure 8 illustrates that the co-occurrence network of STEM competence codes in the EDP-CDIO group was more complex. Furthermore, the mean value of the conventional CDIO group was located on the left-hand side of the coordinates, while that of the EDP-CDIO group was on the right-hand side. According to the locations of STEM competence in the coordinates, the EDP-CDIO group had more segments on knowledge (science, interdisciplinary), skills (creativity, communication, critical thinking), and attitudes (science, technology). In contrast, the conventional CDIO group had more segments on knowledge (technology, mathematics), skills (collaboration), and attitudes (mathematics, engineering, career). Regarding their differences in epistemic connections, the difference network diagram in Fig. 8 c shows that most of the strong connections belonged to the EDP-CDIO group (red), with more connections in particular around interdisciplinary knowledge, while the conventional CDIO group (blue) had few strong connections among the nodes.

This study aimed to design integrated STEM activities for high school students and then examine the different impacts of the EDP-CDIO model and the conventional CDIO approach on students’ STEM competence, including knowledge, skills, and attitudes. In accordance with the study, the results will be discussed as follows.

Impact of integrating EDP into CDIO on students’ STEM competence

In terms of STEM knowledge, the posttest score of the EDP-CDIO group was significantly higher than the pretest score. Moreover, this study showed that the improvement in STEM knowledge was significantly higher in the EDP-CDIO group than in the conventional CDIO group. The reason for this disparity may be that the problem identification and iterative feature of engineering practice promotes students to have a deeper understanding of STEM concepts in STEM activities (English et al., 2017 ; Park et al., 2018 ) and engages students to constantly reflect on applying knowledge to challenging tasks (English & King, 2019 ). For example, one student in the EDP-CDIO group said: “ When redesigning and reimplementing the ladder, we had to apply interdisciplinary knowledge to deeply reflect on the causes of problems in the initial construction, such as the higher center of gravity leads to structural instability or the coding cause the weights not to be raised high enough. ” This could be also explained by English and King’s ( 2015 ) study which found that “While testing their initial structures, the students’ STEM knowledge became more apparent as they recorded recommendations for improving their designs and how such changes would enhance their structures”.

Regarding STEM skills, students in the EDP-CDIO group performed significantly better than those in the conventional CDIO group, which meant that the EDP-CDIO model had a more positive impact on students’ STEM skills. Previous studies indicated that the CDIO model improved undergraduate students’ critical thinking, problem-solving, creativity, collaboration, and communication skills (Haupt, 2018 ; Leslie et al., 2021 ; Taajamaa et al., 2016 ; Wang et al., 2021 ). However, students in EDP-CDIO activities may have more opportunities to share their ideas with others and develop communication skills in a cooperative environment. Moreover, the EDP-CDIO model may help them develop critical thinking skills in reflection, analysis, and evaluation while confronting failure. In addition, the EDP-CDIO model may encourage students to share and explain their ideas frequently during problem solving; thus, it helps increase students’ creativity.

Regarding overall STEM attitudes, the posttest score of the EDP-CDIO group was significantly higher than the pretest score. In addition, this study also found that the improvement in STEM attitudes in the EDP-CDIO group was significantly greater than that in the conventional CDIO group, especially attitudes towards science, engineering, mathematics, technology, and career. The reason may be that the EDP-CDIO model required students to identify the problem, brainstorm ideas, and encouraged them to improve their artifacts by constantly optimizing the solution and eventually creating the best possible outcome, which led to a successful experience and more enthusiasm and interest. As one student from the EDP-CDIO group mentioned, “ Although the load-bearing capacity of our initial ladder was unsatisfactory, through reflection and reimplementation, our final ladder ranked second in the class. I felt very proud, a great sense of achievement, and developed a deeper thirst for knowledge and interest in various disciplines. ” This finding also supports previous research showing that the engineering design process in integrative STEM education helped improve STEM attitudes when students encountered challenges (Guzey et al., 2016 ; Mohd Shahali et al., 2016 ). However, our findings showed that there was no significant difference in the change in students’ attitudes toward “interdisciplinary” learning between the two groups. One of the possible reasons is that both the EDP-CDIO model and conventional CDIO approach emphasize ‘learning by doing’ and encourage students to apply interdisciplinary knowledge and skills to solve real-world problems and challenges, especially within the same engineering problematic context (Crawley et al., 2007 ).

According to the ENA results, students’ STEM competence in both groups was improved in all dimensions. However, the two groups had significant differences in the epistemic network. More specifically, students in the EDP-CDIO group had a more complex network with connections and had more segments and stronger connections, and in particular, more connections around interdisciplinary knowledge. These results strengthen the main finding of this study: EDP-CDIO activities improved students’ STEM competence more significantly than the conventional CDIO activities. These epistemic network findings also provide statistical warrants for our discourse analysis and contribute to our understanding of the differences in the epistemic network characteristics of STEM competence between the two groups. From a methodology perspective, the present study extends the recent research on discourse analysis through ENA in STEM education (Wu et al., 2019a , 2019b ; Wu et al., 2019a , 2019b ).

In summary, both the EDP-CDIO model and the CDIO approach improved the STEM competence of high school students in the integrated STEM activities. This finding also supports previous research that engineering practice can significantly improve students’ STEM competence (Hu et al., 2020 ). However, compared with CDIO approach, the EDP-CDIO model is more effective in improving high school students’ STEM competence, including STEM knowledge, skills, and attitudes. The main reason is probably that students have more opportunities to deepen their understanding of STEM concepts, practice their STEM skills and enhance their attitudes towards STEM during the learning activities of searching and analyzing information, brainstorming, redesigning and re-implementing, etc.

Implications for K-12 STEM education

There are three main aspects of implications in this study. First, since this is still an early attempt in exploring the effect of the EDP-CDIO model in K-12 integrated STEM education, the study could provide a vital reference for K-12 STEM teachers, encouraging them to implement the EDP-CDIO model in the classroom, especially with an emphasis on the problem identification and iterative design process. Teachers, especially engineering and technology teachers, need to first design real-world integrated STEM projects that enable students to engage in active learning, and then provide various scaffolds and facilitate the high-order thinking skills during the eight phases of the EDP-CDIO model. Second, due to the neglect of engineering practice in K-12 STEM education (English, 2017 ; Strimel & Grubbs, 2016 ), there is an urgent need to propose an effective engineering design framework to raise the STEM competence of K-12 students. The present study found that the EDP-CDIO model was more effective than the conventional CDIO approach in K-12 integrated STEM activities, which implies that CDIO, a widely used engineering design framework in higher education, could also be adopted to enhance engineering practice in K-12 STEM education after certain adaptation. Third, a multilevel evaluation approach was applied to assess high school students’ STEM competence before and after interventions, including knowledge-based tests, artifact-based assessments, scale-based self-reports, and interview data. This approach more comprehensively enhances the reliability and validity of the research than the previous study examining the effects of the adapted CDIO engineering design framework on K-12 students (Ma et al., 2020 ).

Conclusion and limitations

This study mainly compared EDP-CDIO activities with the conventional CDIO activities to explore their effectiveness in developing high school students’ STEM competence. Four research questions were studied using a pretest–posttest nonequivalent group design. The results showed that, compared with the conventional CDIO activities, EDP-CDIO (1) significantly improved students’ STEM knowledge and STEM attitudes; (2) significantly enhanced students’ STEM skills; and (3) generated more comprehensive epistemic networks in STEM competence. These findings indicated that the EDP-CDIO model had a more positive impact on improving the STEM competence of high school students than the conventional CDIO approach. Therefore, based on these findings, educators are encouraged to implement the adapted EDP-CDIO engineering design framework in K-12 schools to promote STEM learning.

This study has certain research limitations. First, the qualitative data could be more sufficient since this study did not collect and analyze conversation data while students were building their ladder structure. Second, this study was conducted only in a certain high school, and the findings may need to be further tested in other settings (e.g., with different schools, STEM projects, and class offerings, etc.). Finally, schools need to provide a dedicated makerspace, hands-on materials, and effective professional development to fully implement the EDP-CDIO model. In future studies, we suggest recording students’ group conversation data to analyze the evolving epistemic networks of students’ STEM competence. In addition, researchers could flexibly adapt the EDP-CDIO model in different STEM learning contexts to further examine the influence on students’ STEM competence.

Data availability

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to express their sincere gratitude to Youji Fu and Min Su, who helped with the design of the STEM project; and to Hongying Li and Mingao Yang, who provided general assistance in coding the data for this paper.

This study was supported by Guizhou Province Education Research of Science Planning 2022 project (Project Number: 2022B307).

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Feifei Xi, Hongliang Ma, Junmei Sun & Rucheng Jin

Key Laboratory of Modern Teaching Technology (Ministry of Education), Shaanxi Normal University, Xi’an, 710062, Shaanxi, China

Zhongling Pi

Research Center for AI and STEM Education, Shaanxi Normal University, Xi’an, 710062, Shaanxi, China

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All authors contributed to the study conception and design, and have read and agreed to the published version of the manuscript. FX: Conceptualization, Methodology, Analysis of data and investigation, Writing-Original draft preparation, Writing-Review and Editing. HM: Conceptualization, Methodology, Writing-Original draft preparation, Writing-Review. ZP: Methodology, Analysis of data and investigation, Writing-Review and Editing. YD: Writing-Original draft preparation, Writing-Review and Editing. JS: Writing-Original draft preparation, Writing-Review and Editing. RJ: Writing-Original draft preparation, Writing-Review and Editing.

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Xi, F., Ma, H., Pi, Z. et al. Integrating the engineering design process into the conceive-design-implement-operate model for promoting high school students’ STEM competence. Education Tech Research Dev (2024). https://doi.org/10.1007/s11423-024-10377-7

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Effects of infusing the engineering design process into STEM project-based learning to develop preservice technology teachers’ engineering design thinking

Conclusions

Introduction

Theoretical framework

Research on engineering design thinking

STEM project-based learning combined with engineering design

Research methods

Problem framework

Research subjects

Research tools

Data analysis

Preservice technology teachers’ performance: defining engineering design thinking

Preservice technology teachers’ performance: system concepts in engineering design thinking

Individual system concepts

Preservice technology teachers’ performance: providing examples of engineering design thinking

Preservice technology teachers’ performance: explanations of advanced system concepts

Ability to provide advanced explanations for each system concept

Does EDP-STEM-PBL improve preservice technology teachers’ performance during modeling?

Does EDP-STEM-PBL improve preservice technology teachers’ performance during redesign?

Does EDP-STEM-PBL improve preservice technology teachers’ ability to use sophisticated thinking?

Research limitations

EDP-STEM-PBL improved problem definition, idea generation, and engineering design thinking

Prioritize procedural learning in modeling and redesign for preservice technology teachers in STEM project-based learning

Suggestions for future research

Availability of data and materials

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Integrating the engineering design process into the conceive-design-implement-operate model for promoting high school students’ STEM competence

Cite this article

Introduction

Literature review

Integrating the engineering design process into the CDIO model

STEM competence and its assessments

Purpose and research questions

Theoretical framework

Identify (identify the problem)

Search (search and analyze information)

Brainstorm (brainstorm and share ideas)

Design (design possible solutions)

Select (select an optimal solution)

Implement (build and test the product)