A paradigm shift: Adoption of disruptive learning innovations in an ODL environment: The case of the University of South Africa
Bibliographic record
Abstract
The aim of this article is to shed some light on patterns of and major motives for the adoption of different types of disruptive learning innovations by Unisa academics. To realise the aim of the study, the following questions were addressed: What are the reasons for adopting disruptive learning innovations? What is the level of interaction with disruptive innovations? What training do Unisa academics require on disruptive innovations? A qualitative approach was adopted by conducting focus group interviews with 76 Unisa academics. The data was analysed using open and axial coding, where dominant themes from the discussions were identified and discussed in detail. The findings show that the interaction of Unisa lecturers with different technologies varied from technology to technology. The study also found that disruptive innovations play a pivotal role in opening avenues and collapsing the transactional distance in an ODL institution. Some lecturers lack skill in using some technology, which is a cause for concern. Therefore, lecturers need to be trained in using technology and develop a good understanding of it to improve teaching and learning.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".