Enhancing Education Quality: The Transformative Role of ICT in Modern Teaching and Learning
Bibliographic record
Abstract
Objective: This study examines the impact of information and communication technology (ICT) adoption on the quality of education, focusing on its influence on academic performance and student engagement in different educational institutions.Methods: Data were analysed using SPSS to explore the relationships between ICT adoption, academic performance and student engagement. A sample of 420 educational institutions in Thoothukudi, including primary, secondary and higher education levels, was selected using purposive sampling. Data were collected from institutional records, government education databases, and surveys of 35,000 students and 2,500 educators over the period 2019-2024. Regression analysis was used to assess the influence of ICT on student performance, with a focus on the effectiveness of ICT integration in modern teaching practices.Results: The results indicate a positive correlation between ICT integration and academic performance. Schools with advanced ICT tools showed higher student engagement, especially at the secondary and tertiary levels. However, challenges related to infrastructure and teacher preparedness were identified as barriers to effective ICT use.Novelty: This study provides new insights into how ICT adoption varies across educational levels and the specific challenges faced by different institutions, particularly in rural areas.Theoretical and policy implications: The findings emphasise the importance of improving ICT infrastructure and teacher training. Policy makers are encouraged to focus on equitable distribution of ICT and support for educators to improve learning outcomes through technology
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".