IMMERSIVE LEARNING THROUGH AUGMENTED REALITY: REDEFINING SKILL DEVELOPMENT IN TVET
Bibliographic record
Abstract
The integration of Augmented Reality (AR) in Technical and Vocational Education and Training (TVET) offers significant potential to transform conventional teaching into immersive, skill-focused learning experiences. Yet, the absence of structured pedagogical models constrains its effective implementation in skills-based contexts. Guided by the Design and Development Research (DDR) methodology, this study was conducted in three phases: needs analysis, model construction, and usability evaluation. A total of 120 TVET instructors and nine expert panels from accredited Malaysian training institutions participated. Data collection employed surveys, focus group discussions, and validation workshops, while data analysis applied Thematic Analysis, Interpretive Structural Modelling (ISM), and the Fuzzy Delphi Method (FDM). Results revealed six critical pedagogical elements: interactivity, simulation, performance feedback, remote expert guidance, annotation, and practice-based assessment, achieving expert consensus levels above 80%, with overall agreement reaching 92.3%. The validated AR-T Model encompasses 24 instructional activities categorised under three domains: Content, Instructional Activities, and Assessment. Usability testing confirmed the model’s practicality and contextual relevance in enhancing engagement, instructional clarity, and hands-on competency development. The findings not only contribute a novel structured framework for AR integration in TVET pedagogy but also underscore the urgency of policy support, infrastructural readiness, and continuous professional development for instructors. This research provides critical implications for curriculum developers and policymakers in embedding immersive technologies into skills training, thereby aligning with Industry 4.0 workforce demands.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".