Predicting University Dropout trough Data Mining: A systematic Literature
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
Objectives: To make a systematic review of literature on the prediction of university student dropout through data mining techniques. Methods/Analysis: The study was developed as a systematic review of the literature of empirical research results regarding the prediction of university dropout. In this phase, the review protocol, the selection requirements for potential studies and the method for analyzing the content of the selected studies were provided. The classification presented in section 3 allowed answering the main research question. What are the aspects considered in the prediction of university student desertion through data mining? Findings: University dropout is a problem which affects universities around the world, with consequences such as reduced enrolment, reduced revenue for the university, and financial losses for the State which funds the studies, and also constitutes a social problem for students, their families, and society in general. Hence the importance of predicting university dropout, that is to say identify dropout students in advance, in order to design strategies to tackle this problem. Novelty /Improvement: This is the first work to perform an integral systematic literature review about university dropout prediction through data mining, with studies from 2006–2018. Keywords: Data Mining, Dropout Factors, Dropout Prediction, Machine Learning, University Student Dropout
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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