Empirical Analysis on Factors Impacting on Intention to Use M-learning in Basic Education in Egypt
Bibliographic record
Abstract
It is apparent that m-learning will continuously have a massive role in terms of development in teaching and learning methods for education. Student's intention to use this technology is the main factor that eventually leads to a success in implementing m-learning. The objectives of this particular research are to come up with the development and examination towards a research model to uncover the factors that have important effects on the intention to use mobile learning for basic education in Egypt. A research model was developed through extending the unified theory of acceptance and use of technology (UTAUT) by incorporating two additional factors namely; learners' autonomy (LA) and content quality design (CQD). A quantitative approach based on cross-sectional survey was used to collect data from 386 respondents.. The methodology used in this study was a Partial Least Squares (PLS) that was expected to test the model empirically. The results showcased that learners' autonomy (LA), performance expectancy (PE), facilitating conditions (FC), and social influence (SI) are significant in relation to behavioural intention (BI) to use m-learning while effort expectancy (EE) did not show the impact on intention to use mobile learning. The research also found that content quality design (CQD) affects significantly on performance expectancy (PE) and effort expectancy (EE). The possible development in future research and the limitations of the findings are also discussed later in this paper.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".