Peering into large lectures: examining peer and expert mark agreement using peerScholar, an online peer assessment tool
Bibliographic record
Abstract
Abstract As class sizes increase, methods of assessments shift from costly traditional approaches (e.g. expert‐graded writing assignments) to more economic and logistically feasible methods (e.g. multiple‐choice testing, computer‐automated scoring, or peer assessment). While each method of assessment has its merits, it is peer assessment in particular, especially when made available online through a Web‐based interface (e.g. our peerScholar system), that has the potential to allow a reintegration of open‐ended writing assignments in any size class – and in a manner that is pedagogically superior to traditional approaches. Many benefits are associated with peer assessment, but it was the concerns that prompted two experimental studies ( n = 120 in each) using peerScholar to examine mark agreement between and within groups of expert (graduate teaching assistants) and peer (undergraduate students) markers. Overall, using peerScholar accomplished the goal of returning writing into a large class, while producing grades similar in level and rank order as those provided by expert graders, especially when a grade accountability feature was used.
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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 | 0.019 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".