Retrieval practice – a tool to be able to retain higher mathematics even 3 months after the exam
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
It is a common phenomenon that students forget the learned material within a few days after their exam. A considerable part of university students do not gain long-term knowledge. Aiming to reduce forgetting and increase further retention in a first-year mathematics course for mathematics pre-service teachers, we applied a special kind of retrieval practice in their lessons. The positive effects of retrieval practice – the strategic use of retrieval to enhance memory – have been shown in the medium term in learning university mathematics. In this paper, we investigate the potential benefit of the applied retrieval practice in learning Number Theory at the university level, focusing on knowledge lasting for 3 months. N = 42 first-year pre-service mathematics teacher students wrote a post-test on the material they learned in the course Number Theory three months after their exam. According to our results, those, who learned Number Theory by retrieval practice, performed significantly better than those who learned on the traditional way. Our findings suggest that retrieval practice can have a powerful, long-lasting effect on learning and solving complex mathematical problems.
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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 | 0.037 | 0.020 |
| Meta-epidemiology (narrow) | 0.013 | 0.011 |
| Meta-epidemiology (broad) | 0.016 | 0.007 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.017 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.026 |
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