The extent of and reasons for non re-enrolment: A case of Korea National Open University
Bibliographic record
Abstract
Despite continuous efforts to increase retention, dropout rates are high in distance universities. The objectives of this study were: 1) to investigate the extent and causes of non re-enrollment at a mega university, Korea National Open University; and 2) to suggest actions to improve the retention of students, in general, and those with higher risks of dropout in particular. A survey designed to establish the student demographics and the students’ main reasons for non re-enrollment was carried out during spring, 2009 with 1,353 respondents. The results indicate that a lack of feedback from the instructors, heavy workload, and difficulties in studying at a distance were the main reasons for non re-enrollment. The learners’ perceptions of the value of the degrees and their ages, gender, and educational backgrounds were also found to be significant factors in decisions not to re-enroll. The suggested solutions for reducing non re-enrollment include: a decrease in the number of required credit hours’ study per semester; the provision of stronger social support; the introduction of a more flexible enrollment system; and better use of the available technology and infrastructure to help both students and instructors build stronger learning communities.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".