Online Learning Amidst COVID-19 Emergency: A Case of the University of Malawi’s School of Education
Bibliographic record
Abstract
This paper explores faculty members’ concerns and level of preparedness for open and distance learning (ODL) at the University of Malawi’s School of Education during the recent Covid-19 pandemic within a context that considers ODL as a means of mitigating the impact of the pandemic on teaching and learning. Data were gathered through semi-structured interviews with four experienced academic leaders within the school of education. The Concerns Based Adoption Model (CBAM), particularly stages of concerns, served as a framework to understand the faculty’s concerns about the implementation of ODL initiatives. Inductive and deductive analysis approaches were used to analyse the interview transcripts to identify emerging themes. Deductive analysis revealed that faculty members expressed several concerns such as awareness, informational, as well as consequences concerns as they talked about their feelings and attitudes towards the implementation of ODL. Inductive analysis on the other hand revealed that faculty members’ perceptions such as minimal preparation, negative orientations, and lack of policy awareness hamper the implementation of ODL. These findings underscore the importance of members’ orientation change to ensure effective implementation of ODL in contexts like the institution under study. We discuss these and propose that professional development could help members develop positive attitudes towards ODL.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".