Retrieval Practice for Improving Long-Term Retention in Anatomical Education: A Quasi-Experimental Study
Bibliographic record
Abstract
Abstract It is generally assumed by students that learning takes place during repeated episodes of rereading and rote memorization of course materials. Over the past few decades, however, research has increasingly indicated that the said notion can and should be enhanced with learning paradigms such as retrieval practice (RP). RP occurs when students practice retrieving their consolidated semantic memories by informally testing themselves. This strategy results in the re-encoding and re-consolidation of existing semantic memories, thus strengthening their schemas. The purpose of this quasi-experimental design was to assess the effects of the implementation of RP on student performance on the final exam in a large, undergraduate Gross Anatomy course. It was hypothesized that student participation in RP during class would improve their performance on the final exam in the course. The participants (N = 248) were mainly in Life Sciences, Kinesiology, and Physical Education programs. They answered RP questions using TopHat©, an online educational software platform. The results of this study indicated that student performance on the final exam was enhanced when students engaged in RP. It was concluded that the use of RP effectively enhances learning and long-term retention of semantic memory. In addition to the traditional testing ‘of’ learning, teachers are encouraged to implement testing, in the form of RP, in their classrooms ‘for’ learning.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".