Effect of Integrating Animation Videos into Science Instruction in Under-Resourced Rural Nigerian Schools
Bibliographic record
Abstract
This study investigates the effect of integrating animation videos into science instruction on students’ academic achievement in rural Nigerian secondary schools, where access to educational technology such as electricity supplies is limited. While global interest in video-based pedagogy is increasing, empirical research on its use in under-resourced African classrooms remains limited. This quasi-experimental study involved 83 Junior Secondary Two students from two intact classes. The experimental group received traditional science instruction supported by animation videos preloaded on the teacher's smartphone, while the control group received lecture-only instruction. Guided by three hypotheses, achievement was measured using two validated research instruments: a researcher-adapted Science Aptitude Test (SAT) and the Basic Science Achievement Test (BSAT), with reliability coefficients of 0.711 and 0.68, respectively. Independent samples t-tests revealed statistically significant differences in achievement favoring the animation-supported instruction. Further analysis showed a significant interaction between instructional method and student ability level, with higher gains among both high- and low-ability students in the experimental group. The findings underscore the value of integrating context-appropriate video resources into classroom teaching to support differentiated learning, even in infrastructure-limited settings. This study has implications for teacher training, curriculum development and low-cost technology integration and it contributes to a nuanced understanding of learning processes in marginalized educational contexts such as rural communities in developing countries like Nigeria.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".