Modelling Academic Risks of Students in a Polytechnic System with the Use of Discriminant Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research work “Modelling Academic risks of students in a Polytechnic System with the Use of Discriminant Analysis”: A case Study of Federal Polytechnic Ilaro, Ogun State, identified students at academic risks i.e. those who are in danger of failing, repeating on probation or being withdrawn due to the level of their academic performance. Several methods exist for student’s identification for academic risks; these include the Bayesian approach, Von Mises (Minimax), Multiple Regression Analysis, etc. For this research work, the method adopted was the discriminant analysis which assist in classifying students into classes of grades i.e. Distinction, upper credit, Lower Credit, Pass and others who are in the risk group, the method was adopted due to its simplicity and its systemic classification of the phenomenon under study.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it