Understanding and Readiness in Facing IR 4.0 Future Skills Transformation among UPM Trainee Counsellors
Bibliographic record
Abstract
Industrial Revolution (IR) 4.0 refers to the integration of technology within a particular industry, which encompasses big data, data analytics, cloud computing, robots, artificial intelligence, as well as Internet of Things (IoT) technologies. This study aims to assess the understanding of trainee counsellors of IR4.0 future skills, and to investigate how trainee counsellors improve their overall understanding of IR4.0 and its readiness. The qualitative research design employed in this study involved semi-structured interviews. Four trainee counsellors were chosen through convenient sampling and interviewed in 15-20 minute sessions. The findings indicate that trainee counsellors have a strong grasp of IR4.0. The respondents acknowledged, however, that their comprehension of IR4.0 in connection with future counselling professions is only modest, owing to the institution’s lack of formal educational exposure. Consequently, the respondents’ readiness to face IR4.0 is dangerously low, with the majority expressing worry towards their adaptability in future career development. Finally, the study concluded that educational institutions are vital in teaching and equipping students to confront the global challenges presented by IR4.0. This study is important because it aids researchers to analyse information on the understanding, readiness, and effect of IR4.0 on future skills among trainee counsellors. Additionally, it helps educational institutions in recognising the essential role of IR4.0 adoption in teaching and learning, as well as the implementation of the necessary measures to increase the readiness for training counsellors in tackling IR4.0.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".