Mobile Learning as a Method of Ubiquitous Learning: Students’ Attitudes, Readiness, and Possible Barriers to Implementation in Higher Education
Bibliographic record
Abstract
The purpose of this study was to explore the attitudes and level of readiness, and possible barriers to implementing Mobile Learning as a part of ubiquitous learning. In addition, the study attempted to find out to what extent students are interested in mobile learning. It also aimed to answer the question regarding the readiness of college students to use mobile learning technologies. Furthermore, the level of students experience in electronic learning was examined. The study was conducted to gather valuable data about are the possible advantages and disadvantages of mobile learning, and the barriers do students expect facing when implementing the mobile learning technologies. To answer the research questions, a questionnaire was administered to 1000 college students, with some of them being interviewed for in-depth information. The findings of the study showed that students had highly positive attitudes toward mobile learning, and they had the necessary technical knowledge to implement mobile learning. However, students were found to have very little experience in electronic and mobile learning. Students have mentioned some advantages of mobile learning among which was the possibility of learning outside the classroom and at any time. Some disadvantages were mentioned such as the fact that students might become annoyed with receiving too many text messages per day. Finally, students listed some barriers they expect to face the implementation of mobile learning. The study concluded with suggestions for future research and recommendations to university officials to better implement mobile learning.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".