Mobile learning medium for junior high math interest: how is it developing? Is it effective?
Bibliographic record
Abstract
In today's digital era, education requires innovation to enhance students' interest in learning, especially in mathematics, which is often perceived as challenging. This study targets the development of mobile learning-based media for MTs students in Pasuruan Regency, aiming to boost student engagement and participation in mathematics. Nine grade IX students participated as subjects in this study. Employing the 4D development model (Define, Design, Develop, Disseminate), the research sought to create learning media that is valid, practical, and effective. Instruments included questionnaires to evaluate student effectiveness and interest, alongside expert validation for content and media. The findings revealed that the mobile learning media achieved rigorous standards, with positive feedback from both experts and students. Notably, 78% of students reported that the media facilitated a better understanding of mathematics topics such as algebra and geometry, which were previously difficult. Additionally, 85% of students exhibited increased interest in learning following the use of this media. The data further indicated a 25% improvement in mathematics comprehension post-implementation. These results suggest that the mobile learning media is effective in enhancing both interest and understanding of mathematics, making it a viable tool for educational settings. This study makes a valuable contribution to enriching learning methodologies that are accessible anytime, anywhere, offering students greater learning flexibility.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".