Investigating students' behavioral intention to use mobile learning in higher education in UAE during Coronavirus-19 pandemic
Bibliographic record
Abstract
The study explores the impacts of fear emotions on technology adoption by teachers and students during the COVID-19 pandemic. Mobile learning (ML) has been considered an educational, social platform in private and public higher education institutes. Since several fears are connected with COVID-19, this study's key hypotheses are related to how COVID-19 influences Mobile Learning (ML) adoption. Educators, teachers, and students may face some common types of fear in the course of the coronavirus pandemic, such as fear of losing social relationships, fear of educational loss and failure, and fear because of the lockdown of the family in the prevailing circumstances. Different theoretical models, named Expectation-Confirmation Model (ECM) and Technology Acceptance Model (TAM), are combined to develop an integrated model for this study. The proposed model was analyzed with the development of a questionnaire survey. The survey served as a data collection instrument to collect data from students of the University of Sharjah in Sharjah city in the United Arab Emirates (UAE). Three hundred twenty undergraduate students participated in the study. The collected data was evaluated using the partial least squares-structural equation modeling (PLS-SEM). The significant predictors revealed by experimental results included perceived fear, perceived ease of use, expectation confirmation, satisfaction, and perceived usefulness, explaining the intention to use the mobile learning platform. According to our study, teaching and learning can be benefitted to a great extent by the adoption of mobile learning (ML) during this pandemic for educational purposes; however, this process may be negatively affected by the fear of future educational results, fear of losing social relations and fear of stressful family situations. Therefore, appropriate student evaluation may be conducted to overcome the emotional distress caused by the pandemic effectively.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".