Exploring the long-term effects: Retention and transfer of skills in gamified learning environment
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
This study looks at how gamifying the classroom might help students retain and apply what they have learned in the Jordan educational system. During the year-long research, 500 participants from a wide range of educational attainment levels served as participants. Immediately after participation in gamified courses, participants retain a significant proportion of their newly acquired skills over a long period of time, demonstrating a notable improvement in retention. Important factors that affect how well one remembers newly acquired abilities include intrinsic motivation and interest. What's more, studies have shown that there's a strong link between keeping knowledge and being able to use it elsewhere, which highlights the need of maintaining competence for maximum efficiency in applying knowledge in the real world. Important implications for the Jordan educational system may be drawn from the findings since they are consistent with the goals of Vision 2030. The goal of this nationwide effort is to train workers who can sustainably advance the nation's economy and culture.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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