Bibliographic record
Abstract
The ability to collaborate online is an essential skill that allows learners to bring together diverse perspectives to deepen understanding regardless of physical location. As online learning and hybrid workplaces have become more prevalent since the COVID-19 global pandemic, the critical nature of this skill has become particularly relevant for post-secondary graduates. While exams are traditionally viewed as solitary endeavors, collaborative exams offer invaluable opportunities for learners to develop these skills. However, this form of collaborative assessment can be challenging for both learners and educators, and few studies offer guidance for the effective design of collaborative exams. As such, in this paper, we report on the design and implementation of a synchronous collaborative midterm exam in a large first-year undergraduate course. Specifically, we describe how we drew on a theoretical framework of self- and socially shared regulation of learning to design a three-phase exam fostering learners’ engagement in key processes of planning, strategic enactment, and reflection on collaboration processes and products. Finally, we discuss key considerations that arose during the design and implementation of the exam, including ensuring an emphasis on process and authenticity, ethical use of video, and equity of access.
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.
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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".