Peer Code Review Platform for University Courses

The problem instructors have with peer code review

Students learn a great deal from reading other people's code and almost never do it. In industry, code review is a daily skill, yet most graduates first meet it at their first job. Instructors know this and still skip it, because allocation, anonymity, chasing non participants and checking that reviews are serious is more administration than the exercise is worth when done by hand for a class of 60 or 200.

What already exists and the gap

• Kritik is a peer assessment platform with calibration and feedback on feedback, integrated with LMSs.
• FeedbackFruits Peer Review runs inside Canvas, Moodle, Blackboard or Brightspace as an LTI tool with rubric based anonymous review.
• CrowdGrader lets students submit and collaboratively grade homework.
• Peergrade was a popular option until it was acquired in 2023 and shut down for higher education.

These tools are general purpose: essays, reports and presentations. Code is a second class citizen, usually a file download with no line comments, no syntax highlighting and no automated checks. They are also paid, institution level purchases that many universities do not have. The gap is a code-first peer review tool a single instructor can run for one course.

What you build

Three roles:

• Student: submits, reviews the assigned submissions, reads and rates reviews of their own work.
• Teaching assistant: monitors progress and flagged reviews.
• Instructor: creates assignments and rubrics, runs allocation, audits quality and exports grades.

Main flow: submit by the deadline, allocation runs, review window opens, reviews submitted, authors rate reviews, instructor audits and releases grades.

The technically interesting part

Allocation. Every submission needs a similar number of reviews, nobody reviews their own work, pairings should not repeat across assignments, and late submissions arrive after allocation has run. Model it as a constrained assignment problem (a flow network or a randomised greedy with repair) and prove the fairness properties with tests.

Review quality control. Left unchecked, peer review degrades into every submission receiving "looks good" and full marks. Detect it: very short reviews, no inline comments, reviewers whose scores never vary, and reviewers far from the consensus. Show your supervisor the signals and how well they match the instructor's own judgement.

Aggregation. Median or trimmed mean with outlier handling, compared with instructor marks on a sample.

How to evaluate it

• Classroom run: with the instructor's permission, use it on one real assignment, even a small one. Report participation, on-time reviews, review length and inline comment distributions.
• Agreement: correlation between aggregated peer scores and instructor marks on at least 30 submissions.
• Flagging: instructor labels 50 reviews as useful or low effort; report precision and recall of your flags.
• Allocation: tests over 1,000 random classes checking balance, no self review and no repeats.
• Student survey: a short survey on whether reading others' code helped.

Suggested plan

1. Week 1: meet an instructor who would run a trial; agree the assignment and rubric.
2. Weeks 2 to 4: assignments, submissions, file tree viewer.
3. Weeks 5 to 7: allocation algorithm with tests, review interface with line comments.
4. Weeks 8 to 10: feedback on feedback, quality flags, aggregation, export.
5. Weeks 11 to 14: classroom run, analysis, write up.

Path to a product

Your first customer is a programming instructor in your own department. Free for individual instructors with a paid department plan is a realistic model, as is LTI integration later so it works inside the university LMS. Bootcamps and online programming courses have the same problem and buy faster than universities.

Scope warning

Do not build a learning management system, plagiarism detection or an autograder. Assume the course lives elsewhere; this tool handles one workflow well. Automated checks stay a stretch goal.

Learn what you need on Sefism

• Full-stack roadmap : the web app, database and auth you need.
• Fullstack cheat sheet : quick reference while building.
• Code Review Best Practices : what good review looks like, for your rubric design.
• Conventional Comments : a comment format that makes peer reviews more useful.
• How to give code review feedback without conflict : a good guide to share with your students.

Get help with this idea

This works best when an instructor agrees to a trial early. Tauseef can help you design the allocation algorithm, choose quality signals, and plan a classroom evaluation examiners will respect. If you are new here, create a free account first.

• Discuss this idea with a mentor
• Send a query to Tauseef
• Book a 1:1 session