Call Up Approach: An Intervention Program Driving Effects on Learners’ Performance in the Core Subject Areas
Bibliographic record
Abstract
This study examined the effectiveness of CALL UP Approach, Teachers’ Calibrating action for Lifelong Learning to Upstage Pandemic, on pupils’ performance specifically in English, Mathematics and Science. CALL UP Approach is an intervention strategy formulated by the teacher and employed during the Modular Distance Learning; a modality implemented during the occurrence of Covid 19 pandemic. The quasi-experimental method of research was employed to investigate the effectiveness of CALL UP Approach to improve learners’ performance in English, Math and Science. This pre-posttest design makes use of two group of learners with almost similar characteristics. The learners were divided into two groups to compose the control and experimental groups. The control group were subjected to the usual treatment. Meanwhile, the experimental group were subjected to CALL UP Approach composed of home visits, small group community teaching, scaffolding and feedbacking. The results yielded significant difference on the mean gain score between the control and experimental groups implying that the CALL UP Approach is more effective in improving the pupil’s academic performance in English, Math and Science than the previously available learning model of delivery. The Call Up Approach as an intervention program is found effective to improve the learner’s academic performance as revealed in this study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".