Effect of Visualised Case-Based Learning Strategy On Students’ Academic Performance in Chemistry in Ibadan Metropolis, Nigeria
Bibliographic record
Abstract
This study investigated the effect of the use of visualized case-based learning (VCBL) strategy on chemistry students’ academic achievement. The theoretical framework for this study is based on Thorndike’s idea of transfer of learning. A sample of one hundred and forty-five (145) senior secondary school II chemistry students drawn from four intact classes in two local government areas of Ibadan metropolis, were used for the research. Three well validated instruments were used to collect data. The VCBL package was developed following the Smith and Ragan Instructional System Design (ISD) Model (1999). This model comprises four stages: namely, Analysis, Design, Development and Implementation/Evaluation. Data were analysed by means of inferential statistics (ANCOVA, EMM and Tukey’s post-hoc). Results showed that there is significant main effect of treatment on students’ achievement in Chemistry (F (2, 248) =17.539; p<0.05; η2=0.124); implying that the posttest scores of students’ achievement in achievement significantly differ between the treatment and conventional groups. It was concluded that VCBL strategy has the potential to enable students understand chemistry better by way of promoting transfer of learning. In light of this, implications were discussed and relevant suggestions made.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".