Effectiveness of Course Portfolio in Improving Course Quality at Higher Education
Bibliographic record
Abstract
To fulfill the demand of teaching and learning quality in Higher Education, different means of evaluating, assessing, and accrediting academic programs have evolved. The need arises on finding scientific tools to measure and assess quality at different stages of educational processes. In Higher Education, course portfolio is considered one of essential quality assurance tools used. It is used to monitor and develop activities, to help students construct knowledge, and to improve the academic activities. This paper tackles the effectiveness of such tool for improving learning and teaching processes College of Health and Sport Sciences, University of Bahrain. The results of this study showed that the college faculty have a positive perceptions towards the use of course portfolio. They also, positively perceive the usefulness of audit results of the course portfolio and show good intention towards using electronic course portfolio; however, they need more training and support to use it effectively. In this study, the benefits of course portfolio as an independent variable was found to be a significant predictor of e-portfolio acceptance. College of Health and Sport Sciences need to improve the implementation of e-portfolio system through continuous faculty feedback and improvement plans.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".