NURTURING AND PROMOTING LEARNERS’ RETENTION IN ASTROPHYSICS USING VIDEO-BASED MULTIMEDIA
Bibliographic record
Abstract
Learners’ retention of concepts in physics has been a crucial fact in students’ achievement needs competencies. Therefore, innovative and friendly strategies to develop learners’ retention of physics have been a matter of concern over the years. This study, therefore, strives to investigate the power of video-based multimedia (VBM) to nurture and promote learners’ retention in astrophysics in selected schools in Rwanda. 294 students were purposively selected for the scientific option with physics as a major subject in Rutsiro and Rubavu districts. The astrophysics achievement test with Cronbach alpha 0.87 was used to collect data within the pre/post-test non-equivalent control group quasi-experiment research design. The output revealed that the VBM intervention group outperformed the usual teaching control group. The results also showed that females retained marginally more than males in VBM intervention classes, and urban students retained better than their rural counterparts. Based on the output of this study, we concluded that credit goes to VBM as a friendly and innovative teaching method. Hence, we recommend using VBM in teaching and learning physics with special attention in rural-based schools. Article visualizations:
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".