Strengths and Weaknesses of Education 4.0 in the Higher Education Institution
Bibliographic record
Abstract
The Malaysian Higher Education has implemented an education 4.0 program in line with the 4th industrial revolution. The education 4.0 program is aimed at providing graduates with the capabilities and competencies required by the digital-driven industry. The purpose of this paper is to discuss the strengths and weaknesses of education 4.0 in Malaysia education industry. Lectures in a selected organisation are chosen for data collection purposes. Data was collected through interviews and. Data obtained through interviews and focus group discussions session is analysed using content and analytic induction analysis. Data are sorted and categorised into themes to theorized the strengths and weaknesses of Education 4.0 in Malaysia. The findings of this study found that education 4.0 creates an opportunity for educators to engage in new technology tools and it enhances the knowledge of the educators on technology more in depth. It also helps lecturers and students to enhance their knowledge & usage of technology in depth. In addition, it promotes the development of technology classroom into the 21st century skills. However, there is high resistance to change in adapting and shift the mind set of lecturers towards adopting technology-based education as it can limit the engagement or involvement of an educator with the students. Technology is also found to be disconnecting learners from the real world. This study provides insights of the strengths and weaknesses of education 4.0 to the Ministry of Higher Education Malaysia and to the academics so that strategies in maximising the strengths and strategies in overcoming the weaknesses of education 4.0 can be developed
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".