Perceived Motivational Effects of Mobile Learning Technique to Higher Education Students: An Exploratory Study
Bibliographic record
Abstract
This article aims to examine how adopting the M-Learning technique affects students' intrinsic motivation and ability to learn new material. All in all, 283 higher education students of the University of Education. Twice they were evaluated to see how they fared. Ten multiple-choice questions were utilized for the evaluation, all of which were administered using the Socrative mobile apps. An evaluation form was utilized to get students' feedback on the experiment. According to responses from respondents all of the University of Education, M-Learning creates a more positive classroom atmosphere (71 percent), boosts attendance rates (80 percent), and aids in the retention of material studied (72 percent). All groups' aggregate performance improved as they used the app more often (initial-final evaluation: 5.8 vs. 7.2 points). The results imply that the M-Learning approach is a valuable instrument for enhancing the teaching-learning process and is helpful in the academic setting as a facilitator of knowledge absorption.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".