The Acceptance of Mobile Learning: A Case Study of 3D Simulation Android App for Learning Physics
Bibliographic record
Abstract
This study investigates the adoption of the mobile learning, 3D simulation Android app, as an innovative tool for learning in physics for high school students. The factors affecting the acceptance of mobile learning are also determined in this study. The proposed research model employs two constructs, Perceived Usefulness and Perceived Ease of Use, from Technology Acceptance Model (TAM) as a baseline and adds another relevant factor based on prior mobile learning adoption research, Perceived Enjoyment. Data are collected through questionnaires distributed to 50 high school students using Google Forms. Structural equation modeling (SEM) is used to analyze and develop the research model. The study finds that Perceived Enjoyment becomes the most influencing factor considered by high school students to use mobile learning, 3D simulation Android app, followed by Perceived Usefulness. However, the Perceived Ease of Use factor does not significantly influence the acceptance of the 3D simulation Android app.
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How this classification was reachedexpand
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".