The Effect of Flash-Based Learning Media on Students’ Achievement in Learning Atomic Structure in Kenyan Senior High Schools
Bibliographic record
Abstract
Purpose of the study: This study aims to investigate the effect of Flash-based learning media on students’ learning achievement in chemistry, particularly on the topic of atomic structure, in Kenyan senior high schools. Methodology: The study employed a quasi-experimental research design using a pretest–posttest control group approach. The participants consisted of 84 Grade 10 students from a public senior high school in Kenya, divided into an experimental group and a control group. The experimental group was taught using Flash-based learning media featuring animated visualizations of atomic models, subatomic particles, and electron configurations, while the control group received conventional instruction. Data were collected using a validated chemistry achievement test and analyzed using an independent samples t-test. Main Findings: The results showed that students who learned using Flash-based media achieved significantly higher posttest scores than those taught using conventional methods. The statistical analysis revealed a significant difference between the experimental and control groups (t = 3.24, p < 0.05), indicating that Flash-based learning media had a positive effect on students’ achievement in learning atomic structure. Novelty/Originality of this study: This study provides empirical evidence on the effectiveness of Flash-based learning media in chemistry education within the Kenyan context, which remains underexplored. The findings highlight the potential of interactive multimedia to enhance students’ conceptual understanding of abstract chemistry topics and support the integration of digital learning media in secondary science education in developing countries.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".