Peer Evaluation: Enhancing learning Opportunities and Reducing Marking Effort
Bibliographic record
Abstract
Abstract Evaluation of report-based assignments, especially in larger classes, adds a considerable marking load. Even with detailed rubrics, subjectivity may lead to grading variations and inaccuracies. Evaluation of others’ work can also be a very informative and educational experience, improving their skill through exposure to a broader performance range. Involving students in peer evaluation can potentially address both of these issues by reducing marking load, providing alternate (and increased number of) assessments, and by exposing students to a broader spectrum of report skills thus enhancing their own knowledge. This paper discusses the results of an experiment in peer assessment and whether it can be exploited to reduce marking effort, improve accuracy for report assignment evaluation and improve student skill. The data was gathered from assignments in two different engineering classes: a second year course on safety and environmental stewardship, and a senior course on engineering economics. For the second-year course, an individual essay assignment was marked by the instructor and two peers. The three evaluations were analyzed to assess the accuracy and assign a grade. For the senior course, a group report on a case study was self and peer evaluated. These evaluations were used to derive a grade for the report directly if the self and peer results were within a prescribed tolerance; other cases were resolved by instructor intervention. The results were analyzed considering the number of outliers, range of scores, and the number of cases which had to be resolved by theinstructor. Parameters considered in assessing the results of the experiment included: the correlation between assessments, the learning opportunities for students, and instructor marking effort required. (preliminary analysis) Results suggest positive gains in reducing effort. Improved accuracy and enhanced student learning are also expected.
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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 | 0.025 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".