Peer Evaluation: Enhancing learning Opportunities and Reducing Marking Effort
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it