Designing Mnemonics Serious Games to Promote Knowledge Retention in Memory-Intensive Courses
Bibliographic record
Abstract
The increased difficulty of memory-intensive courses due to many factors necessitates using technological tools to promote retrieval practice and long-term learning. Hence, a mnemonics game, based on the RADAR framework proposed by Oyibo, was implemented to foster knowledge retention. Fifty-two students, comprising an experimental group (n = 30) and a control group (n = 22), were recruited to undertake a study, which involved watching a 10-minute Biology lecture on Biology Organization, Cranial Nerves, and Krebs Cycle and taking repeated tests immediately after a 45-minute preparation, one-week gap pre- and post-15-minute revision. In all three tests, students who used the RADAR game performed better in recall than those who did not. Coupled with the study participants stating they found the game easy to use, enjoyable, useful, and trustworthy, and their willingness to adopt it, the experimental group’s better performance highlights the need to incorporate mnemonics-based games in memory-intensive courses to promote long-term 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".