Mobile learning pedagogies : panel discussion
Bibliographic record
Abstract
Mobile learning has been around for twenty years or so, and different pedagogical methods have been (or have not been) employed. Mobile learning research has often been centered around the technical aspects of mobile tools and applications and less on the pedagogical aspect and learning approaches. While several theories of learning have been applied in mobile learning, the link be-tween theory and pedagogy is often missing, as is the specific relationship be-tween pedagogy and mobile learning theory and practice. Mobile learning mediates any pedagogy in specific ways that may render it qualitatively different from the same pedagogical approach used in another context. However, an important question is whether mobile learning pedagogy can be seen as distinct from other pedagogies. While it is evident that mobile devices can assist traditional pedagogies, such as teaching practices informed by social constructivism, or shifting the focus from teacher-centred to student-centred learning, the question behind these uses of mobile devices in learning is whether there is an identifiable mobile learning pedagogy that is novel and distinct. Discussion is needed to provide a more unified and consistent view of mobile learning and its associated theories and pedagogies, and perhaps bring in new aspects of mobile learning that take account of the opportunities and affordances of evolving mobile technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 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 | 0.012 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.026 | 0.013 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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".