Statistical Modeling of Students’ Academic Performances: A Longitudinal Study
Bibliographic record
Abstract
Within students’ attrition studies, it is necessary to assess the longitudinal evolution of students within a given course of study, from enrolment to exit from the university through degree completion and academic dropout. Here, the student's academic progress is monitored through the number of courses failed each semester enrolled. The students’ failure rate and academic behavior typically provide significant insight into students’ exit outcomes from University programs. These programs usually have a maximum time frame required to complete the course. A likelihood-based approach is discussed that conditions on the exit outcome and random effects in adjusting within-subject correlation of longitudinal measurements. Ignoring the number of courses enrolled by a student may produce inadequate results on the actual failure rates. Conditioned on the exit outcomes of the student, we find out that factors such as financial aid, matriculation points, students’ race and course type registered, and gender are distinguishing factors that affect students’ academic performances, for completers and dropouts. Also, being in university-type accommodation (that often have added services such as transportation and internet connections) does not seem to significantly affect the failure rate within both groups of students. In addition, an increase in matriculation points significantly reduces the failure rate independent of the Quintile school of the student. Several count models such as mixed Poisson, mixed Zero Inflation Poisson, mixed Negative Binomial, and mixed Hurdle Poisson models are fitted and compared. In particular, the mixed Poisson model provides a better fit based on the Bayesian Information Criterion and residues analysis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".