Mobile technologies for learning: Exploring critical mobile learning literacies as enabler of graduateness in a South African research‐led University
Bibliographic record
Abstract
Abstract At Stellenbosch University there is a drive to integrate the development of graduate attributes and the use of emerging technologies in the curriculum. With the aim of discovering the role of emerging mobile technologies in learning a qualitative research project was undertaken with a senior‐student cohort. An inductive thematic analysis was done using Ng's () mLearning literacies framework (cognitive, socio‐emotional and technical), and situating it within the field of graduateness (Barrie ; Bozalek & Watters, ). This paper reports on the research which informs the literature on graduateness with regards to the potential role of critical mobile learning literacies and expands the application of the mLearning literacies framework as part of the digital literacies debate. Resulting themes were: (1) a critical awareness of 21st century learning; (2) an underdeveloped mLearning literacy (with criticality as indicator); and (3) multidimensional expectations regarding the development of mLearning literacy. To support the notion of lifelong learning and graduateness, we call for the development of particularly criticality in mLearning literacy skills at a cognitive, socio‐emotional and technical level with mobile devices in both formal and informal learning. This has implications for curriculum design, pedagogic approaches and a focus on interactions with new forms of knowledge.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".