Graduate Student’s Perception on Effectiveness of Virtual Education during Covid-19: Evidence from Structural Equation Modelling in Nepal
Bibliographic record
Abstract
Background: The COVID-19 pandemic forced educational institutions worldwide to shift to online learning, disrupting traditional academic calendars. In Nepal, this transition significantly impacted graduate students, raising concerns about the effectiveness of virtual education. Objective: This study aims to examine the effectiveness of virtual education on student satisfaction and performance during the COVID-19 pandemic among graduate students in the Kathmandu Valley. Method: Based on the achievement goal theory, 203 graduate students from management colleges in Kathmandu Valley were selected through purposive sampling. Data were collected using a structured questionnaire administered via KOBO Toolbox. Both descriptive and inferential analyses, including Structural Equation Modeling (SEM), were employed to analyze the data. Result: The findings reveal that while the majority of students showed a positive attitude toward online classes, dissatisfaction arose due to a lack of training and familiarity with new information technologies. The SEM results indicate that course design, prompt feedback, and student expectations significantly influence student satisfaction, which in turn mediates the relationship between these factors and student performance. Challenges such as power outages, difficulty concentrating, lack of access to technology and insufficient instructor knowledge were identified as major obstacles. Conclusion: Although graduate students in Kathmandu Valley reported a generally positive outlook towards virtual education, several significant challenges need to be addressed to improve the effectiveness of online learning, particularly in terms of instructor quality and technological infrastructure. Paper Types: Research Paper Keywords: COVID-19, Virtual Education, Management Student, Effectiveness, Graduate Students Perception JEL Classification: D83, I20, L86, A23
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".