Exploratory Factor Analysis of a survey on group-exam experiences and subsequent investigation of the role of group familiarity
Bibliographic record
Abstract
We report on an exploratory study in which we investigate the factor structure of an in-development survey on student experiences during group exams and subsequently examine how these factors can be modelled using performance and self-reported performance measures, while focusing on the role of group familiarity because it is a measure we felt we could bolster through future intervention.We ran an Exploratory Factor Analysis on a suite of survey items that sought to investigate aspects of their group-exam experience, such as participation equity, the prevalence of productive group-work behaviours, and their personal experiences within the group.After stepwise item removal took us from an item pool of twenty-one down to fourteen, our Exploratory Factor Analysis saw a four-factor structure emerge as the preferred option, consistent with the dominant areas of focus of our underlying survey design.The four factors that emerged-presence of under-contributors, presence of dominators, productive group-work behaviours and personal experience-and the items that were removed as part of the factor analysis process indicate directions for future item development.These results suggest that our survey could be sensitive to the impact of interventions designed to improve overall student experience in group exams by targeting improvements in sense of academic familiarity with their groupmates, participation equity or productive group-work behaviours.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".