Evaluating the Implementation of Quality Principles in Competence-Based Assessment at Private TVET Colleges: Perspectives, Differences, Influencing Factors, and Impacts on Student Competence
Bibliographic record
Abstract
This study evaluates the implementation of quality principles in CBA at private TVET colleges in the Amhara Regional State. It employed a sequential explanatory mixed-methods approach, incorporating questionnaires, interviews, and document analysis. A questionnaire was distributed to 873 students and 189 teachers and assessors, including 47 items on a five-point Likert scale. Interviews involved 5 teachers, 4 assessors, 5 students, 2 deans, and 1 assessment center coordinator. The analysis included descriptive statistics such as proportions, means, and standard deviations, while inferential analysis utilized one-sample t-tests, Mann-Whitney U tests, and logistic regression. Qualitative data were analyzed through thematic and content analysis to gain a comprehensive understanding of CBA implementation. The findings indicated a significantly low implementation of CBA across all nine quality principles, with no substantial difference in perceptions between teachers and students. This overall low implementation adversely affected the quality of assessment and student competence. Key challenges identified included unethical interference of regulatory bodies and colleges, inadequate assessment literacy, unethical behavior among teachers and assessors, weak educational backgrounds, and dishonest conduct among students. The insufficient implementation of CBA principles in private TVET colleges complicates the relationship between students’ perceptions of implementation and their competence. Consequently, it is essential to provide professional support for educational assessment approaches to regulatory bodies, colleges, assessors, and teachers. Standardizing the assessments of IOCA, NOCA, and ROCA is critical, along with clearly distinguishing the roles of teachers, assessment tool developers, and assessors to enhance assessment quality.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".