In-lecture quizzes improve online learning for university and community college students
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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