Comparing Large‐Scale Assessments in Two Proctoring Modalities with Interactive Log Data Analysis
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 With the increased restrictions on physical distancing due to the COVID‐19 pandemic, remote proctoring has emerged as an alternative to traditional onsite proctoring to ensure the continuity of essential assessments, such as computer‐based medical licensing exams. Recent literature has highlighted the significant impact of different proctoring modalities on examinees’ test experience, including factors like response‐time data. However, the potential influence of these differences on test performance has remained unclear. One limitation in the current literature is the lack of a rigorous learning analytics framework to evaluate the comparability of computer‐based exams delivered using various proctoring settings. To address this gap, the current study aims to introduce a machine‐learning‐based framework that analyzes computer‐generated response‐time data to investigate the association between proctoring modalities in high‐stakes assessments. We demonstrated the effectiveness of this framework using empirical data collected from a large‐scale high‐stakes medical licensing exam conducted in Canada. By applying the machine‐learning‐based framework, we were able to extract examinee‐specific response‐time data for each proctoring modality and identify distinct time‐use patterns among examinees based on their proctoring modality.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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