The entrepreneurial shift in education: The critical success factors of mobile learning in higher education institutions
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
The objective of this investigation is to analyze the correlation among students' readiness for mobile learning, regulation of emotions, nomophobia, cyberloafing via smartphones, and addiction to smartphones while attending classes amidst the COVID-19 pandemic. Current research introduces a theoretical framework that outlines the factors influencing cyberloafing within the m-learning setting. The study involved a total of 719 participants. The structural equation modelling technique was utilized to evaluate a study's framework. The study's results suggest a significant association between the factors of m-learning readiness, emotion regulation, nomophobia, smartphone cyberloafing, and smartphone addiction among learners. The current study also introduces a conceptual framework for this entrepreneurial shift that outlines the factors influencing cyberloafing within the m-learning setting. The discourse pertains to the ramifications for both students and institutions of higher learning.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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