Exploring the Impact of Technology-Enhanced Learning on Student Engagement and Academic Performance in Indonesian Primary Schools
Bibliographic record
Abstract
This study explores the impact of technology-enhanced learning (TEL) on student engagement and academic performance in Indonesian primary schools. With the increasing integration of digital tools in education, it is essential to understand how TEL influences students' learning experiences and outcomes. The research adopts a mixed-methods approach, incorporating both quantitative data from standardized assessments and qualitative insights from interviews with teachers and students. Findings indicate that TEL significantly improves student engagement by fostering interactive and personalized learning environments. Additionally, the use of technology was found to enhance academic performance, particularly in subjects such as mathematics and language arts, where digital tools provide immediate feedback and adaptive learning opportunities. The study highlights the importance of teacher training and infrastructure development to maximize the benefits of TEL in primary education. Implications for policymakers and educators include the need for continuous investment in digital resources and pedagogical strategies to support technology-driven learning initiatives. This research contributes to the growing body of literature on the role of technology in enhancing educational quality in the Indonesian context.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".