Factors Affecting the Intention to Adopt M-Learning
Bibliographic record
Abstract
Over the recent years, emerging technological applications have been used for making student learning more effective and interactive. M-Learning has been one such technological initiative which has shown promising benefits in the higher education context. Even though the importance of mobile learning has been researched by many, the factors influencing mobile learning adoption intention has not been addressed sufficiently, particularly in the Sri Lankan context. Hence, the purpose of this paper was to present a conceptual model to examine the factors influencing the intention to adopt mobile learning by the students engaged in higher education. Based on a comprehensive literature review, this study extended the Technology Acceptance Model (TAM) (Davis, 1989) with mobile self-efficacy, intrinsic motivation to use mobile devices and the system quality of the m-Learning system. The model describes how the aforementioned factors influence the higher education students’ intention to adopt m-Learning via survey data collected from 151 postgraduate students. The findings suggest that the model explained the factors influencing the intention to adopt m-Learning among students in higher education. In detail, the mobile self -efficacy, system quality and intrinsic motivation significantly influenced the intention to adopt m-Learning. The results could be utilized for increasing the adoption of m-Learning practices and developing mobile applications useful for teaching and learning purposes. This study has incorporated three independent constructs in extending the TAM model; namely, system quality, mobile self-efficacy and intrinsic motivation. These were derived from the IS Success theory, Self-efficacy theory and Self-determination theory respectively. Accordingly, this study intends to address the theoretical gap in the higher education context pertaining to the adoption of mobile learning. Since Mobile Self-Efficacy and System Quality were the most significant factors that affect the perceived ease of use and perceived usefulness, these factors should be given prominence when developing mobile enabled Learning Management Systems within institutions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".