Impact of Higher Education Learning and Teaching Course as an Academic Confirmation Practice in Public University
Bibliographic record
Abstract
The expansion and transformation of Malaysian universities have generated major changes in higher education institutions. These changes have considerable implications on the policy and the practice of academic confirmation in public universities. For new academic staff in one of the higher education institutions in Malaysia, they are required to successfully complete the Higher Education Learning and Teaching Course as one of the requirements for confirmation in the post. It is a good effort on the part of the university to provide knowledge and guidance to new academic staff and at the same time to support their academic development by providing continuous learning on teaching and learning activities. However, there were some concerns regarding the implementation of this course. Some academic staff faces problem due to stress and increased workload. Therefore, the objectives of this research are (i) to explore students’ understanding of the Higher Education Learning and Teaching Course objectives, (ii) to identify the benefits and difficulties faced by the students during the course, (iii) to explore the impact of the course on new academic staff’s teaching and learning activities, and finally (iv) to suggest recommendations to improve the delivery of the course for the benefit of all academic staff. Engaging in pure qualitative research (phenomenology and case study approach), this study methodology procedure was divided into three main stages, (i) library-based research for the collection of secondary data and reviewing them, (ii) fieldwork data collection in the form of an open-ended questionnaire with 18 respondents, and (iii) analysis of the open-ended questionnaire and documents for the purpose of reporting by using thematic analysis. The study found that the students believed that the purpose of the course is to enhance their teaching and learning skills as well as to produce a dynamic and holistic academician. It is undeniable that these students have benefited from this course in terms of knowledge transfer, teaching and learning a soft skill, and building a network which have left a positive impact on their teaching and learning activities. However, there were some difficulties with the heavy workloads during the course which has led to some students feeling demotivated and affecting their mental health. Finally, the study proposes revising the structure of the course by considering reducing the workload, revising the duration of the course, and review of some components in the staffs’ yearly performance appraisal (key performance index (KPI)). The study also found that there is a need for effective communication between the management and academic staff regarding the policy for this higher education teaching and learning course.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".