Predicting graduate student performance – A case study
Bibliographic record
Abstract
Graduate admission has become critical for quality assurance. An innovative solution is needed to achieve efficiency and consistency in the admission process at institutions. However, the existing research is lack of a simple practical method for informed decision-making for admissions. We develop a multi-criteria decision-making (MCDM) model to calculate the probability of success in course-based graduate studies in engineering. This approach integrates a wide range of quality indicators found in the existing literature. We hypothesise that the model will improve post-graduate admission decisions.A case study was conducted on students enrolled in the Master of Applied Science (MASc) in Oil and Gas Engineering programme at EnBeyond University. The results show no significant difference between the calculated chances of success and the obtained average. The proposed model is effective in predicting the chances of success. It will help to prioritise admission opportunities to those who have higher chances of success in graduate studies. It can also help to build a pre-assessment system with which prospective students can assess their admissibility before filing an application. This study is a first step towards a plethora of future research to come in this important area.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".