Impact of open inquiry instructional strategy on secondary school students’ academic achievement and conceptual knowledge in Chemistry across genders in Osun state, Nigeria
Bibliographic record
Abstract
Student performance in chemistry remains below expectations despite the subject’s importance in advancing fields such as medicine and technology. Traditional teaching methods have been criticised for limiting engagement and critical thinking. This study investigates the impact of the open inquiry instructional strategy on secondary school students’ academic achievement and conceptual knowledge in chemistry. A quasi-experimental design was adopted, involving 322 Senior Secondary School 1 science students drawn from intact classes of the selected schools. Students were divided into experimental and control groups and were taught using open inquiry and the traditional lecture method, respectively. Data were collected using the Chemistry Academic Achievement Test (CAAT) and Chemistry Conceptual Knowledge Test (CCKT), both of which were validated before use. Analysis of Covariance (ANCOVA) was used to test the hypotheses. The findings revealed that students taught using open inquiry significantly outperformed those taught using the traditional lecture method in academic achievement (F(1, 317) = 29.083, p < .001) and conceptual knowledge (F(1, 317) = 60.574, p < .001). However, gender differences were not statistically significant for both academic achievement (F(1, 317) = 0.704, p = .402) and conceptual knowledge (F(1, 317) = 2.634, p = .106). The study concludes that open inquiry is a more effective instructional strategy for improving students’ learning outcomes in chemistry, regardless of gender. It recommends the integration of open inquiry into science curricula and teacher training programmes to enhance student engagement and conceptual understanding.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".