Impact of Technology in Classrooms in the Colleges of Kathmandu: Challenges and Policy Recommendations
Bibliographic record
Abstract
The integration of technology in classrooms has transformed educational practices globally, accelerated notably by the COVID-19 pandemic. This study examines the impact of technology in colleges throughout Kathmandu, Nepal, focusing on both its benefits and challenges. Technology, including platforms like Google Classroom and educational apps, has significantly enhanced engagement and learning experiences for students, as highlighted by educators and learners surveyed. However, persistent challenges such as inadequate infrastructure, limited access to devices, and technical issues like unreliable internet connectivity hinder widespread adoption and effective use. Through a mixed-methods approach, utilizing surveys and narrative analysis, the article interprets experiences and insights from teachers and students and discusses themes that underscore the critical role of technology in improving educational access and quality in Nepal. Teachers express varying degrees of confidence and readiness in integrating technology, while students report increased engagement and improved learning outcomes facilitated by digital tools. The discussed results reflect on both the challenges and opportunities of technology in classrooms which suggest Policy recommendations, such as enhancing infrastructure investment, providing professional development for educators, and fostering digital literacy among students to bridge the digital divide and maximize the benefits of technology in education. This research contributes to understanding the nuanced dynamics of technology integration in a developing country context, offering insights for policymakers, educators, and stakeholders to optimize educational practices and ensure inclusive access to quality education in Kathmandu and beyond.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".