The Effect of Direct Instruction Strategy on Math Achievement of Primary 4th and 5th Grade Students with Learning Difficulties
Bibliographic record
Abstract
This study seeks to verify the effect of direct instruction strategy on Math achievment of students with learning difficulties in the fourth and fifth grade levels and measure the improvement in their attitudes to Mathematics.Sample consisted of sixty (60) students with Math learning difficulties attending 4th and 5th grade level resource rooms recruited from six School Districts within the metropolitan Directorate of Education. Participants were randomly assigned to control (N=30) and experimental (N=30) groups. Experimental students received training on basic Math skills using the Direct Instruction Strategy, whereas the control groups students were taght traditionally.Achievement test were built to measure basic mathematical skill among fourth and fifth grade students as a pre-test and post-test and validity and reliability coefficients were secured. To measure student attitudes to mathematics, attitudes to mathematics scale was developed and tested for validity and reliability.The achievement test was administered as pre-test and post-test, Results from the statistical analysis indicated a perceived effect of the direct instruction strategy on basic skills achievement of fourth and fifth grade students with learning difficulties and improved their attitudes to mathematics. To identify basic skills achievement level of fourth and fifth grade students.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".