Learning Gap Assessment in Integrated Mathematics 9
Bibliographic record
Abstract
The pandemic has profoundly impacted education, posing unprecedented challenges that demand immediate attention. Thus, this study was conducted to identify intervention activities that may be introduced on the learning gaps in Integrated Mathematics 9 for the First Quarter of the School Year 2022-2023. A quantitative quasi-experimental research using a pretest-posttest design was employed in this study and conducted on the 31 Grade 9 students of St. Paul University Surigao during the First Quarter of the School Year 2022-2023. A validated test was used to conduct the pretest and posttest to assess the learning gaps in Mathematics 9. Frequency, percentage distribution, and paired t-test were used in analyzing the data gathered. This study found that there are least-mastered competencies in the First Quarter of Mathematics 9. In addition, there is a significant difference in the pre-and posttest performance of the learners, especially after giving intervention activities such as drill, practice exercises, tutoring sessions, or small group instruction, peer tutoring and collaborative learning, expanded opportunity, explicit and technology-assisted instruction. Thus, the intervention improved learner performance and addressed least-mastered competencies. It is recommended for mathematics teachers to design further intervention materials targeting other least-learned competencies.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".