Best Practice for Online Tests: How Long Do Students Actually Need?
Bibliographic record
Abstract
Multiple choice tests are unlikely to disappear from formal education, partly due to the ease of large-scale administration and grading and their similarity to licensing exams in various fields (e.g., nursing). Despite post-secondary instructors’ best intentions in giving students adequate time to complete multiple choice assessments, it can be difficult to judge the amount of time that is actually required by students, while attempting to maintain test integrity and minimize cheating behaviour. Further, much of the available literature on this topic focuses on students enrolled in four-year university programs, which are likely to differ from other post-secondary programs (i.e. two- and three-year diploma programs). The present study aims to quantify the amount of time students in two- and three-year programs actually used to answer multiple choice questions in a fully online, asynchronous, introduction to psychology course, as well as examine whether differences exist in the time used on two types of assessments: small quizzes with unlimited attempts and unit tests with only one attempt. Results showed that students used on average 39 seconds per question, though they used significantly more time on summative assessments (unit tests) compared to formative quizzes. These results can help guide pedagogical decisions, but it is also important to consider learner-specific characteristics which might affect how much time they use (or need) to complete multiple choice assessments.
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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 | 0.010 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".