Exploring Advantages of the Implementation of a Peer-Assessment Tool in a First-Year Undergraduate Course
Bibliographic record
Abstract
Abstract Benefits of peer-assessments have been recognized for quite some time. Research has shown that the impact of peer-evaluations on students' learning experience, is one that is positive and focuses on building critical thinking and metacognitive skills. This research aims to provide insight on the opportunities and effects of integrating peer-assessments in a University of Waterloo first-year engineering course's model as the primary assessment method. To explore this, Kritik, a third-party platform that focuses on peer-learning through peer-assessment, was used as part of a solid mechanics course offered to first year engineering students at the University of Waterloo. In this course, first-year engineering students were required to complete various hands-on lab activities, and a final project as a group. Once submitting their initial work on the platform, they were then required to individually assess their peers' work, and then individually reflect on the grades that they received. The grade they received for their group work, the quality of their peer evaluations, and the feedback they provided on the grades they received from their peers were all factors of their final individual grade for each assessment. This encouraged students to evaluate each other's work in a genuine manner—increasing the viability of their evaluations. The results show an increase in understanding of course concepts, less stress on the teaching team due to reduced grading, and an increase in opportunity for the students to fully experience what the course offers in a realistic time frame—an opportunity that is scarce due to the length of academic terms and the sheer amount of course content to be taught.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".