How factors of personal attitudes and learning environments affect gender difference toward mobile learning acceptance
Bibliographic record
Abstract
<p>Mobile technology for learning empowers the shift from traditionally pure instructor-centered classroom teaching to fully learner-centered educational settings. Although mobile learning (m-learning) accessing Internet resources anytime and anywhere, the issue of the relationship between gender perspectives and m-learning environments has been a controversial topic. Therefore, the main research objective of the research is to explore gender differences toward APP-based m-learning environments. Subjects of the study were selected university students who had already used an APP-based m-learning system for one month. A total of one hundred fifty nine valid questionnaires were collected, spilt evenly between female and male students. The statistical analyses lead to significant conclusions. Both female and male learners have highly positive perceptions toward m-learning. Additionally, the proposed research model is an acceptable conceptual model for investigating female and male learners’ attitudes. Furthermore, factors of personal attitudes and learning environments affect gender difference toward m-learning acceptance. </p>
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".