Factor Analysis on E-Learning Implementation in Mongolian Higher Education
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines the survey results on e-learning implementation, which covered 726 lecturers from Mongolian universities and colleges. The authors determined factors that influence e-learning course classification. We applied methods such as regression analysis and factor analysis. It revealed that institutional factors such as ownership, LMS, size, and personal factors such as age, gender, prior training, team, field of science, qualification, and locations influence the tendency to develop e-learning courses. The study is beneficial for policymakers and practitioners by broadening the understanding of institutional and personal factors influencing e-learning course development. Implications for practice and policy: 1) Mongolian higher education institutions primarily practice web-facilitated courses with few blended and online courses. 2) Institutional factors such as ownership, LMS, size, and personal factors such as age, gender, prior training, team, field of science, qualification, and locations influence the tendency to develop e-learning courses. 3) There is a strong need for faculty development as every third lecturer does not know about the learning theories and does not apply them to the e-learning course development. 4) The higher education management should address challenges faced by the faculty members. 72.65% of respondents raised environment and faculty development challenges.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it