Technology Adoption Readiness in Disadvantaged Universities during COVID-19 Pandemic in South Africa
Bibliographic record
Abstract
The Covid-19 pandemic has affected hundreds of million lives and taken over four million lives to date. As a result, governments and policymakers see the need for emergency action to reduce the spread of the virus. In an attempt to contain the virus, governments and policymakers worldwide introduced a different range of protection measures and interventions to change their citizen's behaviours, primarily through social distancing, interprovince lockdown, stay at home strategies, and quarantines. The different lockdown measures have created unique and challenging conditions with no documented equivalent in the education sector. A significant effect was that many Higher Education institutions worldwide were not ready to switch to online teaching and learning when the governments announced the sudden lockdown. This study discusses the effects of the Covid-19 pandemic on South Africa Higher Education Institutions, focusing on the historically disadvantaged universities. The study went further to evaluate the readiness of lecturers from selected disadvantaged universities to adopting online teaching and learning by applying the Technology Readiness-Acceptance Model (TRAM). Quantitative data was collected through an online questionnaire. Results show that the higher the average of optimism and innovativeness among the respondents' point towards the readiness of adopting technology. On the other hand, higher the average insecurity and discomfort show the uneasiness of adopting technologies by the respondents.
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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 | 0.006 |
| 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 | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".