E-learning Challenges Faced by University Teachers During COVID-19 Pandemic
Bibliographic record
Abstract
The research has explored various challenges related to E-learning dynamics faced by high-education academicians during the COVID-19 period. To develop an in-depth understanding, qualitative research methods were used and the narrative inquiry method was integrated for collecting data from interviewees. Data were collected from 14 university teachers via focus group discussions in which respondents were required to provide written narrations about the teaching-related challenges as well. Data were narrated from the words of respondents using the thematic analysis approach. Based on the analysis, seven major themes were derived and characterized as the key challenges of e-learning during COVID-19 named as lack of readiness, lack of resources and training, lack of interaction and motivation, quality of education deliverance, assessment & monitoring issue, Workload and distractions and Curriculum revision. Accordingly, this research has offered useful suggestions for education policymakers, senior management of universities, and researchers which will enable them to have a better understanding of E-learning challenges in developing countries.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".