Developing a Conceptual Model of Self-Directed Learning in Virtual Environments for Medical Sciences Students
Bibliographic record
Abstract
Identification of key factors affecting the self-directed learning process in the virtual environment of medical education is vital. In this article, we designed a model that describes the self-directed learning process in the virtual learning environment for post graduate students of medical sciences in Iran. This study was carried out in two steps: first, using a qualitative study, we explored the formation of a self-directed learning process in the virtual environment. Second, a review of the literature was conducted to identify the conceptual models. Finally, based on the results, a self-directed learning model for virtual learning was developed. A total of 25 people were research participants in the qualitative part, and individual interviews were conducted with both faculty members and students. There were 1,049 codes, 80 subcategories, 15 categories, and 5 themes extracted from the interviews and through analysis. The themes included (a) backgrounds and requirements, (b) support, discipline, and coordination of the educational system, (c) students’ effort to manage to learn, (d) efficiency, attractiveness, and organization of educational environments and context, and (e) personal excellence, growth, and development. The self-directed learning process in virtual environments consists of some elements and structures, and a description of the relationship between these elements can be the basis of educational planning to develop and compile an effective evaluation of this skill.
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How this classification was reachedexpand
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 | 0.035 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".