The regulation of learning and co-creation of new knowledge in mobile learning
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 devices as learning tools enrich mobile computer supported collaborative learning (mCSCL). Engaging in metacognitive interaction promotes students’ regulatory learning and this can provide a positive influence to learning outcomes. However, despite insightful empirical studies, there is no research into the actual processes of new knowledge creation in this context. This leads to the question of how mobile learning experiences can support the co-creation of new knowledge. Two classroom action research studies were carried out using a qualitative research approach. The analysis of the mobile messages using conversation analysis indicates that self-regulated learning in mCSCL is non-linear, defying existing theory. The findings also show that learners find ways to self-regulate learning activities in socially stimulated learning environments. Through knowledge sharing, students seek new insights into the learning instead of mere transfer of existing content. The Strategic Co-creation of New Knowledge in mCSCL Model has been developed providing innovative ways to approach mobile learning. The findings also comprise improved descriptive models in cross-boundary learning. This research is significant as emerging elements encourage instructors to rethink and design better mobile learning activities to optimize learning. Three recommendations are made and if implemented, will enable learning facilitators to achieve enhanced learning outcomes, engage learners better and improve learning experiences.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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