Student Attrition at Technical and Vocational Educational Training (TVET) Institutions: The Case of XCel Technical College in Malaysia
Bibliographic record
Abstract
Student attrition is a challenging issue for tertiary education institutions, especially Technical and Vocational Education Training (TVET) institutions. There are a lot of explanations why students withdraw from college level programmes and the causes may be unique for students who sign up in a course that suits their interest areas. Small student retention rates reflect negatively on the reputation of the institution and even more, its academic status. This would, in turn, influence institution enrolment, finances, and future plans for development. Thus, this research effort was designed to investigate the influences of students’ withdrawal from these institutions before completion of their studies. As this research took the qualitative approach, data collection was performed through interviews and focus group discussions involving two groups of students (i.e., those who dropped out and those who continued with their studies) from XCel Technical College. The findings showed that the students’ reasons for dropping out from the TVET institutions programme are varied, all which were classified into two categories, namely institutional factors (e.g., training facilities, learning materials, and scheduling) and student characteristics (e.g., parental/family influence and urgency of getting employment). This findings support the results of earlier studies which highlighted that student characteristics, institutional factor, educational and occupational goals and commitments, financial status and other personal factors, are important to their retention in higher education programs (Bafatoom, 2010; Bean, 1980; Braxton, 2005; Pascarella & Terenzini, 1983; Spady, 1970, 1971; Tinto, 1975, 1993).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| 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; both teacher heads agree on what is shown here.
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".