In-lecture quizzes improve online learning for university and community college students
Bibliographic record
Abstract
Online classes are now integral to higher education, particularly for students at two-year community colleges, who are profoundly underrepresented in experimental research. Here, we provided a rigorous test of using interpolated retrieval practice to enhance learning from an online lecture for both university and community college students (N = 703). We manipulated interpolated activity (participants saw review slides or answered short quiz questions) and onscreen distractions (control, memes, TikTok). Our results showed that interpolated retrieval enhanced online learning for both student groups, but this benefit was moderated by onscreen distractions. Surprisingly, the presence of TikTok videos produced an ironic effect of distraction-it enhanced learning for students in the interpolated review condition, allowing them to perform similarly to students who took the interpolated quizzes. Moreover, we showed in an exploratory analysis that the intervention-induced learning improvements were mediated by a composite measure of engaged learning, thus providing a mechanistic account of our findings. Finally, our data provided preliminary evidence that interpolated retrieval practice might reduce the achievement gap for Black students.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".