Evaluating M-Learning System Adoption by Faculty Members in Saudi Arabia Using Concern Based Adoption Model (CBAM) Stages of Concern
Bibliographic record
Abstract
This study assesses the use of an m-learning system by faculty members in Saudi Arabia using a new approach and methodology. Optimum use of educational technology requires consideration of requirements, obstacles and opportunities expected from user interaction with such systems and tools. While the use of m-learning in Saudi Arabia is relatively new, different research studies have investigated the use of m-learning in Saudi Arabia using different models. Most of the presented models investigated the acceptance and use from student perspectives, with little consideration of adoption by faculty members, their use of m-learning systems and their concerns (i.e. facilitators and barriers) as users. Some of the used models managed to provide significant results in relation to m-learning use, while others were found to lack a systematic and appropriate methodology. Concern Based Adoption Model (CBAM), which is widely used in the USA, Canada and (more recently) the Middle East (particularly Jordan), was used in this study to investigate m-learning adoption as an educational technology in Saudi Arabia. This framework provides tools to evaluate the use of educational technology within educational settings. This framework has not previously been used in Saudi Arabian educational research literature, and it is believed that the output will be valuable for enhancing the level of concern, adoption and use of m-learning in the future.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".