Retrieval practice – a tool to be able to retain higher mathematics even 3 months after the exam
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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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".