POWER OF PEERS: EXPERIENCES USING AN ONLINE PEER ASSESSMENT TOOL TO GRADE STUDENT WORK
Bibliographic record
Abstract
Abstract This paper explores the implementation, outcomes, and student perceptions of the use of an online tool for anonymous peer assessment of student work. Peer assessment, where one student assesses the work of another, provides an opportunity for important skill development, as well as a fully-scalable strategy for rich, timely, and frequent feedback. In first and third year engineering courses at the University of British Columbia, we have begun using an online peer assessment tool (peerScholar). The tool divides the peer assessment process into three phases: a creation phase where the work is written or uploaded, an assessment phase where students are randomly assigned to assess the work of a set number of their peers, and a review phase where students review the feedback they received, with options to revise their work or assess the quality of feedback received. We have successfully used this tool in two large (n = 750) classes and one moderate-sized (n = 130) class, with a wide range of different types of student work, including letters, technical memoranda, detailed design reports, and video presentations. Through surveys, student feedback with the tool and the process has been positive. Students at both year levels overwhelmingly recognize the importance of peer assessment—over 90% identified it as an essential skill for an engineer, and over 85% felt opportunities for peer assessment should be embedded in the curriculum. Both groups indicate that they felt the process of reviewing others’ work was beneficial for their own understanding of the material; however, first year students were more likely than third year students to put more effort into their work knowing it would be peer assessed, and that they found the content of the feedback received more helpful to their learning. Student acceptance has been good. In a third year mechanical design course, three different design assignments were independently assessed by students using peerScholar and by teaching assistants. The outcomes across all measures were encouraging: for each assignment, the students and teaching assistants had similar mean, standard deviation, minimum, and maximum values, as well as reasonable correlation (r = 0.5 overall). Overall, we consider the adoption of peerScholar a success. Students have been receptive, challenges have been minor, and feedback is more detailed and frequent.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".