University Students’ Persistence With Technology-Mediated Distance Education: A Response to COVID-19 and Beyond in Zimbabwe
Bibliographic record
Abstract
Technology-mediated distance education (TDE) has become part of the new normal in the range of teaching strategies used in universities in Zimbabwe. Contemporary literature abounds with studies that highlight challenges associated with access to education in universities, yet very little is highlighted about how TDE can be used to enhance access to education in Zimbabwean universities during the COVID-19 era and beyond. The purpose of this study was therefore to investigate determinants of students’ behavioural intentions to persist with TDE in universities in Zimbabwe during COVID-19 and beyond. The study employed a quantitative approach that used a self-constructed structured questionnaire for data collection from a sample of 1,300 distance learning students selected from five universities using a stratified random sampling strategy. Structural equation modelling using IBM SPSS Amos 22 was used for data analysis. Results of the study show that cultural and norms issues (β = .325; p < .001) and characteristics of the students (β = .329; p < .001), the lecturer (β = .362; p < .001), the institution (β = .427; p < .001), and external stakeholders (β = .279; p < .001) were all significantly associated with the behavioural intentions of university students to persist with TDE. Results of this study have implications for both policy and practice with regard to implementing TDE in universities.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".