The regulation of learning and co-creation of new knowledge in mobile learning
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.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".