Technology Acceptance Model of Immersive Microlearning in STEAM Education: Insights from a PLS-SEM Analysis
Bibliographic record
Abstract
This study examines learners' acceptance of an immersive STEAM-based microlearning environment from the perspective of the Technology Acceptance Model (TAM), utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) as the primary analytical approach. While immersive technologies such as virtual reality (VR) and augmented reality (AR) have become increasingly integrated into educational contexts, limited research has explored their adoption within STEAM-focused microlearning settings designed to foster creativity. Drawing on a sample of 40 undergraduate students in Thailand, the study examined the interrelationships among five core TAM constructs: perceived ease of use (PEOU), perceived usefulness (PU), attitude toward using (ATT), behavioral intention to use (BI), and actual system use (USE). The findings reveal that PU significantly influences ATT (β = 0.799) and BI (β = 0.492), while PEOU has a strong effect on PU (β = 0.825) but a negligible direct impact on ATT (β = -0.029). The strongest predictor of actual system use was ATT (β = 0.652), suggesting that positive attitudes toward the learning environment are crucial for sustained engagement. Moreover, indirect effects underscore the mediating role of PU between PEOU and other TAM constructs. The model explained 51.5% to 68.1% of the variance in the endogenous variables, confirming its robustness in this educational context. These findings highlight the importance of emphasizing perceived usefulness and intuitive design in the development of immersive microlearning systems for STEAM education. Implications for instructional design and future research directions are also discussed.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".