Technology Acceptance Model of Immersive Microlearning in STEAM Education: Insights from a PLS-SEM Analysis
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it