Effect of Strategic Intervention Materials on the Learning Competencies of Learners
Bibliographic record
Abstract
In response to the alarming performance of the learners in the core subjects, this study aimed to improve the learning outcomes using the Strategic Intervention Materials (SIM) in Grade 6 Science subject. Two intact classes with a total of forty-six (46) Grade VI learners were used as participants of the quasi-experimental study. By random approach, the control group had 20 learners, and 26 learners in the experimental group. The effect of SIM to the performance of the struggling learners in science subject was determined using the pre-test and post-test scores of the two groups of participants. Appropriate statistical tools were used to analyse the test scores of the participants by observing the assumptions of parametric test. Results showed that the pre-test scores between the two groups of participants at the start were very comparable. However, the post test scores of the participants from the experimental and control groups were statistically different with a medium effect size. Moreover, the performance of the female participants significantly exceeded the performance of their male counterparts. This was due to the diligence of the female participants in keeping and handling of the SIM for their advantage. The researcher concluded that SIM significantly helped improve the test scores of the learners. For this, it is recommended that SIM be also applied to other learning areas in schools.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".