Students Can Learn More Efficiently When Lectures Are Replaced with Practice Opportunities and Feedback
Bibliographic record
Abstract
Many college students drop out of STEM majors after struggling in gateway courses, in part because these courses have large time demands. The risk of attrition is higher for those from financially disadvantaged backgrounds who often work to pay for college, making such time commitments unfeasible. In two laboratory experiments with different topics (central tendency and linear regression), we identified a promising approach to increase the efficiency of STEM instruction. When we removed instructional videos and taught participants exclusively with practice and feedback, they learned 2-3 times faster. However, our research also showed that this instructional strategy has the potential to undermine interest in course content for less-confident students, who may be discouraged when challenged to solve problems without upfront instruction and learn from their mistakes. If researchers and educators can develop engaging and efficacy-building activities that replace lectures, STEM courses could become better, more equitable learning environments.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".