Factors influencing students' attitude toward to use mobile learning applications using SEM-ANN hybrid approach
Bibliographic record
Abstract
Mobile learning application now is considered a powerful application for learning and was adopted in universities in the period of Covid-19. After Covid-19 pandemic, university students have been allowed to use mobile learning systems, it is needed to ensure students’ intention to continuously use mobile learning for their learning activities or not. Thus, the purpose of this paper is to understand the main determinants that encourage the continuous use of mobile learning. To achieve that, we used the UTAUT-2 model to predict the main determinants of mobile learning acceptance. In our study, a quantitative technique was employed to collect the data. A hybrid approach SEM-ANN was applied to validate the research model. The findings indicated that performance expectancy and effort expectancy had a strong effect on students' attitudes towards mobile learning platforms. In addition, the results showed that performance expectancy and effort expectancy have a significant impact on students' continuous intention to use mobile learning platforms after Covid-19. In addition, hedonic motivation and habit had a positive effect on both students' attitudes and continuous intention to use mobile learning platforms. Moreover, Social influence factor and facilitating conditions had a significant effect on students' continuous intention to use mobile learning platforms after Covid-19.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".