how to submit assignment in coursera

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Submit peer reviewed assignments, learner help center nov 29, 2023 • knowledge, article details.

When you submit a peer-reviewed assignment, other learners in the course will review your work and submit feedback . 

You'll also need to give feedback to other learners. Your grade might be affected if you don't give feedback.

If you're having trouble with a peer reviewed assignment, check our troubleshooting page .

Steps to submit

To submit a peer reviewed assignment:

• Open the course you want to submit an assignment for.
• Click the Grades tab.
• Choose the assignment you want to submit work for.
• Read the instructions, then click My submission to submit your assignment.
• To save a draft of your assignment, click Save draft .
• To see what your saved assignment will look like when you submit it, click Preview .
• Before you submit, ensure the assignment is above the minimum word count. The default minimum is five words, but a course may have a unique minimum set.
• To make changes to your saved assignment, click Edit .
• To submit your assignment for peer review, click Submit for review .

By submitting a peer reviewed assignment, you confirm that you understand and will follow our privacy policies about peer reviewed work.

When will I receive feedback from my peers?

You'll receive a grade on your assignment within 7-10 days, as long as you've received at least one peer review.

When you get feedback, you may see the name of the learner who gave it. If your instructor has anonymous feedback turned on, you’ll see a notice at the top of the feedback for the assignment.

I can’t submit my assignment

If you can’t submit your assignment, make sure that your answers are all over the minimum word limit.

You may not be able to submit your assignment if your answers are too similar to another learner’s submission. Please keep in mind that plagiarism is against the Coursera Honor Code. 

If you see a notification letting you know that your assignment answers are similar to another learner’s submission, you’ll need to update your response before submitting. 

Once you’ve updated your answers with original work, the Submit for review button will appear.

If you need more time to work on your assignment, you can click Save draft and come back to it later.

If you think you shouldn’t be seeing this error, you can click the link below the notification to let us know. You’ll be able to submit your assignment after you edit your answers.

If you aren’t seeing any error messages, but are still not able to submit your assignment, try these troubleshooting steps.

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Attempt limits

Some private courses (such as courses in a Degree or MasterTrack program) may have a limit on how many times you can submit a peer-reviewed assignment.

If there's an attempt limit for your assignment, you'll see an 'Attempts' section listed near the top of the page when you open the assignment.

If you meet the attempt limit and need help with your grade, you can reach out to your program support team. You can find your dedicated support email address in the onboarding course for your program.

Save your work as a draft

If you want to start working on an assignment but you don't want to submit it yet, you can save it as a draft. When you save an assignment as a draft:

• You can work on your saved draft from any computer or device if you log in with your Coursera account.
• No one will be able to see or review your work until you submit it.
• You can save a draft as many times as you want before submitting it.

To save an assignment as a draft, click  Save draft when you're working on it.

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Coming soon to all courses: Flexible session-based schedules

August 10, 2016

Over the past year, Coursera has offered courses in two different formats: self-paced (no start or end date) and flexible session-based (regular sessions that begin and end on specific days). On August 15th, we’ll be updating all courses to use the flexible session-based format.

The session-based format offers much of the same flexibility of a self-paced course. The biggest difference is the community – in a session-based course, you’re part of a smaller peer group working on the same schedule, so you can share ideas, discuss assignments, and motivate each other. You’ll see suggested due dates in session-based courses, but there are no grade penalties for missing a due date. If you fall too far behind or need additional time to complete the course after your session ends, you can move into a later session, and your work will automatically transfer with you.

We’re switching to this session-based format based on our observations of various course structures that we’ve tested over the past two years. Compared to learners in self-paced courses, learners enrolled in session-based courses are about 40% more likely to post in discussion forums, receive feedback on peer-graded assignments about 33% faster, and are about 60% more likely to complete the course – we’re making this change in order to offer those benefits to all learners.

If you’re currently enrolled in a self-paced course, you’ll see some changes between August 15th and September 19th as we make this transition. Here’s what you can expect:

1. Your course progress and assignment grades won’t be affected by the switch from self-paced to session-based format, so you’ll be able to continue in the course where you left off. Any posts you’ve made in forums will also be saved.

2. When you visit your course, you’ll see that you’re enrolled in a specific session with a start and end date. You’ll also see suggested due dates to help you complete the course by the session end date.

3. There is no penalty for missing due dates, but we do suggest submitting assignments on time so that you keep up with your peers. If you submit a peer-graded assignment too far past the due date, you may need to switch sessions in order to get reviewed by your peers.

4. You’ll receive due date reminders via email. If you want to stop receiving reminders, you can turn them off by unchecking “Reminders” for any or all of your courses in the “Email Preferences” section of your Account Settings .

If you have more questions about how sessions work, please visit our Learner Help Center . We’ll be continuing to improve the session-based format in the coming months, and we look forward to bringing you an even better learning experience on Coursera.

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How do I get my assignment reviewed on Coursera?

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• Start date Jul 15, 2016

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• Jul 15, 2016

To link your assignment in the forums or in Social Media Groups: Open the course for the assignment you want to share. Click the Assignments tab. Open the assignment you want to share. Click My submission . Click Shareable Link . When your assignment opens, copy the page URL from your browser bar. Make a new forum post that includes the URL to your assignment. Click to expand...

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Graduate Student Instructor - SIADS 644 Spring/Summer 2024

How to apply.

The School of Information has up to 1 position at .25 fraction, pending enrollment, open for applications. 

A .25 fraction GSI position offers a monthly stipend, tuition waiver, and health insurance. This appointment runs from May 1, 2024 to August 31, 2024 (from an employment/health benefit perspective).  The course takes place from 7/26/2024 - 8/22/2024. There will be work before and after the course dates. The expected work commitment is roughly 20 hours per week during this time period (roughly 8 weeks). This workload effort is condensed into the described time period mentioned above but your salary is spread across the entire term.  Thus, you will receive a stipend at the end of each month during the semester (May - August).  All of the work hour details will be spelled out in the fraction calculation form for the person hired for this position.

Please indicate your interest by submitting a cover letter and resume electronically using the careers.umich.edu website. Below are some instructions to help you through this application process.

1.       Go to http://www.umjobs.org

2.       Click on "Login"  (upper right corner).  Use your umich uniqname and password.

3.       Click on "U-M Graduate Student on the Ann Arbor campus" identifying yourself as a UM Graduate Student (fourth option)

4.       Click in the "Search for Jobs" box at the top of the page

5.       Enter the Job Opening ID # 247632

6.       You are now in the standard application. Answer all questions and proceed through the application process as prompted.  Upload your application as one document (preferably a Word or PDF document) including your cover letter with information on availability, your resume, and any teaching evaluations*

7.       Click "Submit" when you are finished.

*Having trouble uploading your document? 

The most common cause of upload and display issues can be attributed to an unsupported operating system or internet browser. Internet Explorer is the browser of choice when using the site, however, if one browser doesn't seem to be working properly, switch to a different browser and/or clear your cache and cookies.

Double check your document type. The system accepts resumes/cover letters created in a .DOC, .DOCX, .PDF, .TXT .HTML or .RTF. Uploading your resume/cover letter as a Microsoft Word document is the recommended format. File names are limited to 35 characters or less and cannot contain punctuation marks or special characters.

Course Description

SIADS 644 - Reinforcement Learning Algorithms COURSE DATES: 7/26/2024 - 8/22/2024 AVAILABLE JOB POSTING: FOR A U-M GRADUATE STUDENT

Number of available GSI positions: Up to 1 position at .25 fraction, pending enrollment 0 position(s) UMSI PhD Students

Applications received Spring/Summer 2023: n/a  

Reinforcement Learning Algorithms --- This course covers the basic principles of reinforcement learning and popular modern reinforcement learning algorithms. Students will develop familiarity with both model-based and model-free reinforcement learning algorithms, including Q-learning, Actor-Critic algorithms, and multi-armed bandit algorithms.

More information about these courses can be found on U-M's Course Catalog via Wolverine Access.

Responsibilities*

Assist in the delivery of UMSI courses on Coursera, grading, holding online office hours, attend weekly staff meetings, managing autograders, answering student questions and communicating clearly with students, and facilitating small group online discussions and student conversations. Demonstrate respect for students as individuals and foster a respectful atmosphere in the online learning environment. This position will also be expected to work collaboratively with lead instructors and other instructional team members.

Required Qualifications*

Graduate student in good standing;  Must meet eligibility criteria as defined in the GEO contract; Must be lawfully able to be employed in the United States. Sponsorship to obtain such status is not available at this time; The applicant must have one or more of the following skill sets: 1.       Programming: proficiency in Python; Jupyter Notebooks; PyTorch, OpenAI.Gym 2.       Mathematics: linear algebra, probability and statistics, dynamic programming, reinforcement learning theory and deep reinforcement learning algorithms.  3.       Experiment Design: Familiar with implementation of deep reinforcement learning algorithms, including DQN, DDQN, DDPG, SAC. Familiar with the OpenAI.Gym environment. 

Desired Qualifications*

Experience or interest in teaching; 

Strong communication and analytical skills; 

Experience teaching programming and technology skills to beginning students. 

Background Screening

The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks.  Background checks are performed in compliance with the Fair Credit Reporting Act.

Contact Information

Please do not contact the faculty member who is teaching the course. Any questions should be directed to [email protected] . 

Decision Making Process

The application due date is April 26th, 2024. All applications will be submitted for review to the faculty member teaching the course. After review of applications and possible interviews, decisions will be made by the faculty member teaching the course.  It is anticipated that decisions will be made by early May.  

Selection Process

Relevant preparation for teaching the course material; 

Extent of prior instructional/work experience relevant to the course and relevant to the GSI requirements for this course; 

Demonstration of explanatory skills; 

Position’s relevance to graduate training; 

Previous student evaluations, if applicable; 

Availability for course time requirements.

GEO Contract Information

The University will not discriminate against any applicant for employment because of race, creed, color, religion, national origin, ancestry, genetic information, marital status, familial status, parental status or pregnancy status, sex, gender identity or expression (whether actual or perceived), sexual orientation, age, height, weight, disability, citizenship status, veteran status, HIV antibody status, political belief, membership in any social or political organization, participation in a grievance or complaint whether formal or informal, medical conditions including those related to pregnancy, childbirth and breastfeeding, arrest record, or any other factor where the item in question will not interfere with job performance and where the employee is otherwise qualified. The University of Michigan agrees to abide by the protections afforded employees with disabilities as outlined in the rules and regulations which implement Section 504 of the Rehabilitation Act of 1973 and the Americans with Disabilities Act.

Information for the Office for Institutional Equity may be found at https://oie.umich.edu/ and for the University Ombuds at https://ombuds.umich.edu/

Unsuccessful applications will be retained for consideration in the event that there are last minute openings for available positions. In the event that an employee does not receive their preferred assignment, they can request a written explanation or an in-person interview with the hiring agents(s) to be scheduled at a mutually agreed upon time.

This position, as posted, is subject to a collective bargaining agreement between the Regents of the University of Michigan and the Graduate Employees' Organization, American Federation of Teachers, AFL-CIO 3550.

Standard Practice Guide 601.38, Required Disclosure of Felony Charges and/or Felony Convictions applies to all Graduate Student Assistants (GSAs). SPG 601.38 may be accessed online at https://spg.umich.edu/policy/601.38 , and its relation to your employment can be found in MOU 10 of your employment contract.

U-M EEO/AA Statement

The University of Michigan is an equal opportunity/affirmative action employer.