Effect of Feedback and Remediation on Students’ Achievement in Junior Secondary School Mathematics
Bibliographic record
Abstract
The study investigated the effects of feedback and remediation as instructional strategies on junior secondary school students’ achievement in mathematics. The effects of gender and socio economic status on these learning outcomes were also examined. The sample for the study consisted of 240 junior secondary two (JSS II) students in intact classes of three co–educational schools purposively selected from Akure South Local Government Area of Ondo State. The study employed quasi–experimental design with treatment at three levels namely: Formative Test with Feedback and Remediation, Formative Test with Feedback only and Formative Test without feedback and remediation which served as control. The treatment levels were crossed with students’ socio economic status (high, medium and low) and gender (male and female). Five research instruments including three Formative Tests I. II and III in Mathematics, Socio Economic Status Questionnaire (SESQ) and Mathematics Achievement Test (MAT) were constructed, validated, and used for the collection of all relevant data. The data collected were analyzed using Analysis of Covariance (ANCOVA) and Scheffe’s Post–Hoc Analysis. Results from the study revealed a significant effect of treatment on students’ achievement in mathematics. However, there were no significant effects of gender and socio economic status (SES) on achievement in mathematics.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".