Bibliographic record
Abstract
The use of spaced repetition within a physics higher education thermodynamics module has been analysed for: its pattern of use by students; its effect on memory and performance in the end of module exam; and performance in a delayed test after the summer vacation. A custom-built web app with the facility to generate a personalised repetition timetable was used to deliver practice questions on the material throughout the module. Just over a quarter of students, spanning the whole ability range of the class, made use of the app in some way, about half using it in a spaced manner and half using it for massed practice just before the exam. Students who engaged in a spaced manner had an adjusted mean exam score of 70%, compared to 64% for massed usage and 61% for non-usage. The spaced usage represents a positive effect size of 0.47 over non-usage, which is statistically significant (p = 0.000056). For the delayed test the mean adjusted scores for spacers and non-users were 45% and 34% respectively. Whilst less material had been retained over the summer, this revealed a statistically significant (p = 0.021) positive effect size of 0.54. This work provides evidence and mechanisms to involve students in repetitive practice during the learning phase of a course to advantage their long term retention of material.
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.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".