STUDENT AND INSTRUCTOR EXPERIENCE USING COLLABORATIVE ANNOTATION VIA PERUSALL IN UPPER YEAR AND GRADUATE COURSES
Bibliographic record
Abstract
Perusall is a collaborative annotation platform designed for pre-readings in a flipped classroom, but can also be used for stand-alone, asynchronous reading discussion components of courses. We examine the use of Perusall as a social constructivist learning tool in two upper year/graduate courses in Mechanical Engineering. Perusall was used to replace in-class discussion of readings during the shift to online teaching. Data was collected from student surveys and from the student and instructor annotations themselves. Annotations were coded for content, and examined for factors such as upvoting. We found substantial engagement from students, with collaborative annotation providing opportunities for: correction of misunderstanding; linking concepts from the course and between readings; discussing larger issues around research and research writing; sharing background information among peers; and critically analyzing the readings. Students reported deeper learning than in typical in-class discussions of readings; however, they also noted that annotation required much more time. Overall, collaborative annotation appears to be an effective method for course reading discussion.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".