Asian learners’ perception of quality in distance education and gender differences
Bibliographic record
Abstract
<p class="Articletitle">There have been few efforts to investigate the concept of quality from learners’ perspectives or to incorporate their needs and perceptions in quality standards in distance education. This is rather surprising, particularly in distance education contexts where the quality of the learning is not derived only from the products and services delivered to the learner but also from the knowledge, understanding, and relationships that are codeveloped by both teachers and learners during the teaching and learning processes. This study proposes and verifies a conceptual model of the 10 dimensions of quality in distance education from the learners’ perspectives and investigates gender differences in the perception of quality in distance education with 1,665 distance learners from 11 Asian countries and one territory. The results show that all 10 dimensions across supportive, pedagogical, and environmental domains in the model are important in judging quality in distance education. Also, gender differences are found in the perceived importance of 10 quality dimensions, barriers to DE, important supporters, and types of support received. The implications of these findings are discussed and suggestions for further research and development are offered.</p>
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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; a candidate call from one teacher head, 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".