Leaders’ Preparedness for Managing Technological Changes in Teaching and Learning in the Selected Tanzanian Public Universities
Bibliographic record
Abstract
Technological changes have seemingly become inexorable rather than the exception for academic institutions. It has been argued that an organisation's ability to adapt to a changing environment depends on its preparedness for change. This article delineates the extent of preparedness for managing technological changes in teaching and learning among mid-level academic leaders (MLALs) in higher education. The survey was administered to MLALs (n=76), undergoing changes relating to the use of innovative teaching and learning technologies (ITLTs). The rating method and Yeh Index of Perception (YIP) score were used to determine the extent of preparedness for technological changes among MLALs, and it was found to be on average. The results from the ANOVA test shows there was a significant difference in the mean scores for the dimensions of preparedness for changes (p<0.05). The results conclude that preparedness for changes is determined by multi-dimension indicators as suggested by diverse managerial competences and status of readiness for changes held by MLALs. The results suggest that MLALs have a relatively low competence level for motivating the adoption and implementation of technological changes in teaching and learning. Besides, the leaders had a low belief that proposed technological changes for innovative teaching and learning were beneficial to them. These results can be used further to design the training and strategies for managing technological changes in education. Therefore, the study proposes sensitisation prior to any implementation of technological changes in education.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".