Mobile learning: Moving past the myths and embracing the opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.<br /><br />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.<br /><br />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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it