An Investigation of University Student Readiness towards M-learning using Technology Acceptance Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>M-learning is<strong> </strong>learning delivered via mobile devices and mobile technology. The research indicates that this medium of learning has potential to enhance formal as well as informal learning. However, acceptance of m-learning greatly depends upon personal attitude of students towards this medium; therefore this study focuses only on the individual context in which role of student’s readiness towards m-learning is investigated using Technology acceptance model (TAM). TAM is the popular choice among the researchers for investigating acceptance of any new technology primarily because of its robust and parsimonious nature. The sample selected for this study consisted of students from the private sector universities in a developing country. A structured questionnaire was used for data collection. The final results of investigation were based on 244 valid responses. The results indicate that the students’ skills and psychological readiness strongly influence their perceived ease of use (PEU) and perceived usefulness (PU) of m-learning, whereas both these constructs positively influenced their behavioral intention to use m-learning. The findings of this study have theoretical as well as practical implications which are discussed at the end.</p>
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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 | 0.014 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it