Modeling Students’ Readiness to Adopt Mobile Learning in Higher Education: An Empirical Study
Bibliographic record
Abstract
Mobile devices are increasingly coming to penetrate people's daily lives. Mobile learning (m-learning) is viewed as key to the coming era of electronic learning (e-learning). In the meantime, the use of mobile devices for learning has made a significant contribution to delivering education among higher education students worldwide. However, while m-learning is being widely adopted in developed countries, the adoption of such an approach in developing countries is still immature and underdeveloped. Developing countries are facing several challenges and lagging behind in terms of adopting m-learning in higher education. Thus, this paper explores the factors that have an impact on students’ intentions and readiness to adopt m-learning in higher education in Jordan. Based on the data collected from the field, we examine Jordanian students' requirements and preferences in terms of m-learning design, and we also investigate their concerns about adopting m-learning. This empirical study collected data from students using a paper-based questionnaire. The results reveal that students' intentions to adopt m-learning is influenced by several factors that include the relative advantage, complexity, social influence, perceived enjoyment, and the self-management of learning. By providing a picture of students' willingness to adopt m-learning, this study offers useful and beneficial implications for developers of m-learning applications and for educational providers to guide the design and implementation of comprehensive m-learning systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".