Towards a Conceptual Framework Highlighting Mobile Learning Challenges
Bibliographic record
Abstract
Over the last decade, there has been much interest in mobile technologies in teaching and learning as emerging and innovative tools. Despite this focus, mobile learning (m-Learning) implementation is facing many challenges. This study presents a tentative conceptual framework that consolidates existing research related to mobile learning implementation barriers. The study adopted a systematic review of the literature on challenges to mobile learning. A total of 125 papers published between 2007 and 2017 were extracted from established peer reviewed journals. A qualitative content analysis was used to define 24 barriers that have been grouped into four conceptual categories: Technological, Learner, Pedagogical and Facilitating Conditions. The proposed framework acts as guide for educators, systems developers, policy makers, researchers and stakeholders interested in implementing mobile learning programs.
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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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.009 | 0.008 |
| 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".