Development and testing of a m-learning system for the professional development of academics through design-based action research
Bibliographic record
Abstract
In the present study, a mobile learning system for the professional development of academics was developed by design based action research, and the perceptions and experiences of the academics using this system were examined. In the first phase of this design-based action research, the research question was defined. In the second phase, a m-learning system called “Mobile Academic Research Support” (MARS) was designed as a solution to the problem, and the IOS mobile application for this design was developed. In the third phase of the study, the MARS application was regularly tested and evaluated by the academics over eight weeks. At the end of the research process, the results were reflected upon. It was found that the primary and important professional development needs of the academics were at the scientific research level. It was also observed that the m-learning system developed for the professional development of the academics regarding scientific research was appropriate to the overall purpose, accessible, adaptable and appealing; that it served both as a m-learning and as an academic support system; that its content was satisfactory; and that the tools used in the system were useful. In addition, it was observed that the academics were able to use mobile technologies for learning. Also, it was stated that such a system could provide positive contributions to the professional development of academics.
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 | 0.038 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".