The Relationship of Web-Based Learning among Academic Staff in TVET Institutions
Bibliographic record
Abstract
The use of web applications at Polytechnic Mukah Sarawak (PMU) involves not only teaching and learning but also the management of the institution. MS Team is fully utilized during COVID-19 among staff and students to facilitate discussion, teaching, and learning sessions. Nonetheless, one of the difficulties that academics and students encounter is internet networking. MS Teams are still used by staff where internal and foreign meetings are held. As a result, this study is being conducted to investigate the relationships between the factors that impact the adoption of Microsoft Teams among PMU instructors. Data were collected in the first quarter of 2022 using a purposive sample approach, and 103 respondents satisfied the study's requirements. The partial Least Squares Structural Modelling approach was used to analyse the data. According to the data processing results, Facilitating Conditions (FC) and Perceived Ease of Usage (PEOU) were significant and had a good influence on the adoption of MS Team among PMU instructors. The implications of these findings are that MS Teams should be improved and maintained for facilitating teaching and learning, meetings, and others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".