Harnessing Information Communication Technology for Enhanced Student Engagement and Mathematics Learning Among Grade 7 Students
Bibliographic record
Abstract
Harnessing of ICT in education plays an increasingly crucial role in tackling issues related to student engagement and mathematics learning. In spite of increased access to digital tools, there is still a need to critically explore how well ICT is utilized to support Grade 7 learners' learning. This research sought to establish the extent of harnessing of ICT as an instructional approach and its relationship with the level of Grade 7 students’ engagement and Mathematics learning at public secondary school in Division of Bohol, Carcar City, and Cebu Province for school year 2024 to 2025. The researchers implemented a descriptive-correlational design where they collected data using an adopted questionnaire and the advisers’ ratings of students in the Third Quarter Mathematics 7. Two hundred eighty-five were randomly chosen through multistage sampling, combining stratified random sampling in one school and total enumeration in the others. Data analysis involved using methods such as percentage, weighted mean, standard deviation, and the Pearson product-moment correlation coefficient. Results indicated that ICT was regularly used for teaching, and this resulted in high levels of both cognitive and behavioral engagement from the students. While ICT enhances students' engagement in and enriches learning experiences, its impact on Mathematics performance remains limited if used solely. The findings reveal that technology does not automatically lead to any improvement in learning unless combined with instructional approaches that can relate the use of ICT to lesson goals. Students were found to engage and perform acceptably, but meaningful improvement requires effective pedagogy coupled with purposeful ICT integration. It is recommended that teachers design lessons that merge ICT tools with clear objectives to ensure technology supports students' mathematical learning.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".