Technology Integration in Education: Opportunities and Challenges
Bibliographic record
Abstract
The integration of technology in education has transformed traditional teaching and learning paradigms, offering new opportunities while presenting significant challenges. This paper explores the role of technology in modern education, focusing on its potential to enhance learning outcomes, increase accessibility, and foster innovation. It also examines the challenges, including digital divides, teacher preparedness, and ethical concerns. The study concludes with recommendations for effective technology integration to maximize its benefits while addressing its limitations. Technology integration in education involves incorporating tools such as computers tablets, and educational software to enhance teaching methods and improve student learning outcomes. The present study examined the challenges and impact of technology integration in the learning process. This study aimed to identify challenges students faced in technology integration and assess the role and impact of technology on student learning. The population included university students from the Faculty of Social Sciences at a public sector university. The study employed a quantitative, descriptive strategy, selecting 100 social sciences students through random sampling. Data collection involved quantitative questionnaires, analyzed with SPSS using descriptive statistics, including frequencies and percentages. The results of this study explored that technology integration is useful for student learning but there are also challenges in using it for learning. The study also found that technology plays a main role in students' lives and greatly impacts their academic performance. The findings suggested that all stakeholders in education should ensure technology is utilized for its potential benefits.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".