Educational Data Mining in Higher Education: Building a Predictive Model for Retaining University Graduates as Master's Students
Bibliographic record
Abstract
The goal of this study was to create a model for predicting the factors that influence graduates’ decisions to continue their studies at the master's level within the same institution. The research was conducted on the entire population of students ( N = 663) who started their studies at the Faculty of Education, University of East Sarajevo between 2008 and 2018 and completed their studies by 2021. Part of the data was collected from the faculty information systems and part through questionnaires. The results showed the artificial neural network had the highest classification accuracy while variables, the personal factors, the faculty offers quality, applicable and useful study programs, time to degree and place of residence have the best predictive value. This model can enable other institutions of higher education to create an inclusive environment that enhances student wellbeing, improves educational results, and increases institutional efficiency.
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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 | 0.014 | 0.002 |
| 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 | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".