Factors Related to Students’ Drop Out of a Distance Language Learning Programme
Bibliographic record
Abstract
This paper presents a study that examined the reasons for dropping out of a distance language learning programmeoffered by an open university in Indonesia. A purposive sample of students who registered for online English writingcourses at the university was used. To gain a better understanding of the issues, the study also sought informationfrom online tutors. A longitudinal research design employing qualitative research method was used over four stagesof data collection. Open-ended question surveys were adopted to gain an understanding of underlying reasons forpersisting or discontinuing their studies. Semi-structured interviews were conducted at each stage to obtain deeperinformation from the students and the online tutors. The data was analysed with NVivo version 10. The findings ofthe open-ended question surveys and the interviews indicated that the major reasons that led the students to drop outwere lack of basic skills in English, unmet expectations, feelings of isolation, and the inability to balance work,family, and study responsibilities. The study offers a theoretical framework to describe the factors related to studentdropout from a distance language learning programme. This study also offers models of interaction, teaching andlearning in distance language learning to minimise the dropout rate.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".