Impacts of Training on Knowledge Dissemination and Application among Academics in Malaysian Institutions of Higher Education
Bibliographic record
Abstract
This paper explores the linkage of knowledge dissemination and the application of new knowledge in teaching and learning practices. A survey data were collected from 519 academics from all the Malaysian public and private institutions of higher learning (IHLs) during the teaching and learning trainings offered by the Academy of Leadership in Higher Education Malaysia, known as the Akademi Kepimpinan Pengajian Tinggi Malaysia (AKEPT). Three out of ten behavioral actions were found to have a significant change in behaviors at the workplace: keep-up with the institutional change process, p=0.037, involvement in departmental change, p=0.027 and confidence in decision-making, p=0.037. The seven insignificant behavioral actions were asking peers and colleagues for suggestions, involvement of colleagues in the change process, reluctance in making decisions, holding group meeting, taking time to transform plan into action, and taking time to reflect the consequences of making decisions. These findings raise awareness and provide initial guidelines for AKEPT to develop appropriate strategies to ensure that the knowledge dissemination processes lead to the application of new knowledge. Further exploration of the formulation of comprehensive strategies to properly implement and manage the knowledge dissemination processes among the academics was also suggested. It is also one of the initial studies that highlight the linkages between AKEPT’s Training Centre and the local teaching and learning training centre. It opens up new lines of future research possibilities on the provision of centralized professional development training programs that facilitate the application of new knowledge at the local teaching and learning context.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".