From physical to virtual: The impact of mixed reality technologies on students' engagement in Kuwait universities using structural equation modeling
Bibliographic record
Abstract
This paper sought to test the impact of mixed reality technologies on student engagement in Kuwait universities. Physical reality, augmented reality, augmented virtuality, and virtual reality have been relied upon as mixed reality technologies. Moreover, behavioural, cognitive, and emotional engagement were used as measures of students' engagement according to self-determination theory. The data used in the analysis were received from 812 students in various disciplines in Kuwaiti universities with a response rate of 86.19%. Structural equation modeling (SEM) was the statistical approach used in data analysis. The results indicated varying relative importance levels for mixed reality techniques, although the relative importance level for students' engagement was high. Besides, all mixed reality technologies had a positive impact on students' engagement, with the highest impact of augmented reality and the lowest impact of augmented virtuality. This paper provided contributions to the development of an empirical approach based on new technologies to improve student engagement in developing country universities. Accordingly, the paper emphasized the need for Kuwaiti universities to invest in augmented reality technologies, for example, interactive screens and 3D mobile applications to increase students' exploratory ability.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".