Mobile learning: Moving past the myths and embracing the opportunities
Bibliographic record
Abstract
Mobile learning (mLearning) in the open and distance learning landscape, holds promise and provides exciting new opportunities. In order to understand and embrace these opportunities within various contexts and circumstances it is imperative to understand the essence of the phenomenon. In this regard, we first need to understand the core fundamentals of mLearning and gain insight in what mLearning entails. Using critical reflection, this paper clarifies what mLearning is by invalidating myths and misperceptions related to mLearning. Acknowledging the lessons learnt through past experience, the authors then explore the opportunities that mLearning provides. mLearning challenges and risks are discussed to assist those who are keen to embrace these opportunities, in avoiding unnecessary risks and pitfalls. The paper concludes by sharing a few thoughts on the future of mLearning. These perspectives on mLearning seek to provide an overview of what mobile learning entails, recognise the achievements of mobile learning to date, and stimulate an appetite to embrace the opportunities in open and distance learning, while minimising the potential negative effects of technological, social and pedagogical change.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.017 | 0.033 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".