Optimizing STEAM-Based Differentiated Instruction to Enhance the Effectiveness of Surah At-Tin Memorization among Fourth-Grade Students at Elementary School
Bibliographic record
Abstract
This study investigated the effectiveness of a differentiated instructional model grounded in STEAM principles, augmented by an interactive PILAR media, on the memorization of Surah At-Tin among fourth-grade elementary students. A true experimental pretest–posttest control-group design was employed, involving an experimental cohort (n=6) and a control cohort (n=9). Both groups completed a baseline assessment of Qur'anic memorization before undergoing four instructional sessions; the experimental group received STEAM-based differentiated activities and digital media support, whereas the control group experienced conventional lecture-based instruction. Posttest results revealed that the experimental group achieved a mean score of 91.67 (SD=8.54), compared to 69.44 (SD=15.32) in the control group. Normalized gain analysis indicated a high gain (g=0.90) for the experimental cohort and a moderate gain (g=0.64) for the control cohort. These findings demonstrated that aligning pedagogical strategies with individual learning preferences, integrating multimodal STEAM tasks, and leveraging interactive technology significantly enhanced both the quantity and accuracy of Qur'anic memorization. The study concluded that a differentiated STEAM-based approach, supported by PILAR media, constituted a superior method for optimizing primary-level Qur'anic memorization and recommended its broader application and longitudinal evaluation.
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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) | 0.000 | 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) | 0.001 | 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".