Computer-Based Science Simulations, Guided-Discovery and Students’ Performance in Chemistry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigated the relative effectiveness of computer-based science simulations on students’ achievement in chemistry at the secondary school level when compared with guided-discovery and the traditional expository teaching methods. The study used non- randomized pre-test – post-test control group design. The study sample was 89 Senior Secondary II (SSII) chemistry students drawn from Uyo Local Government Area, Akwa lbom State, Nigeria. Criterion sampling technique was used for sampling. Two hypotheses were tested. The instrument used in collecting data was a researcher-developed 25-item 4-option multiple choice test - the Chemistry Achievement Test (CAT) - designed to measure students' achievement in the area of chemical combination. The test had a reliability index of 0.72 determined using test-retest approach. The results of data analysis using Analysis of Covariance (ANCOVA) showed that students taught by computer-based science simulations performed significantly better than those taught using the traditional expository method, (mean diff. = 4.34; sig. = .032), but had comparable performance with those taught with guided-discovery approach (mean diff. = .85; sig. =.869). That is, computer based simulation method is as effective as guided-discovery, but significantly better than the traditional expository method; and that gender is not a strong determinant of students' performance in chemistry. Based on the findings, it was recommended, among others, that chemistry teachers should adopt computer-based simulation technique in teaching chemistry concepts in view of its high facilitative effect on students’ performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it