Motivating factors that affect enrolment and student performance in an ODL engineering programme
Bibliographic record
Abstract
The present study was carried out to determine the motivating factors for enrolling in an engineering study programme in open and distance learning (ODL) and the factors that affect the students’ performance. The study was conducted with two convenient samples of students following distance learning courses in engineering technology, conducted by the Open University of Sri Lanka and leading to the award of diploma or degree in Engineering Technology. One sample consisted of students who failed to qualify for sitting the year-end examination and the other consisted of successful students. The study showed that many students opt to follow the programme without knowing the relevance of the study programme for them. Most of the students (53%) had taken the courses believing that they could obtain a recognized degree. The students with secondary education qualifications in the mathematics stream were found to be successful, provided that they had selected the ODL programmes with an understanding of the nature of the ODL system, the percentages being 61 with 4 passes, 75 with 3 passes, and 20 with 2 passes at the General Certificate in Education (Advanced Level) examination. All successful students were of the opinion that the course delivery methods should be improved, especially face-to-face teaching, laboratory classes, and assignments.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".