Interaction of Proctoring and Student Major on Online Test Performance
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
Traditional and online university courses share expectations for quality content and rigor. Student and faculty concerns about compromised academic integrity and actual instances of academic dishonesty in assessments, especially with online testing, are increasingly troublesome. Recent research suggests that in the absence of proctoring, the time taken to complete an exam increases significantly and online test results are inflated. This study uses a randomized design in seven sections of an online course to examine test scores from 97 students and time taken to complete online tests with and without proctoring software, controlling for exam difficulty, course design, instructor effects, and student majors. Results from fixed effects estimated from a fitted statistical model showed a significant advantage in quiz performance (7-9 points on a 100 point quiz) when students were not proctored, with all other variables statistically accounted for. Larger grade disparities and longer testing times were observed on the most difficult quizzes, and with factors that reflected the perception of high stakes of the quiz grades. Overall, use of proctoring software resulted in lower quiz scores, shorter quiz taking times, and less variation in quiz performance across exams, implying greater compliance with academic integrity compared with when quizzes were taken without proctoring software.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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