An ID3 Decision Tree Algorithm-Based Model for Predicting Student Performance Using Comprehensive Student Selection Data at Telkom University
Bibliographic record
Abstract
Telkom University, in its routine admission process, generates a rich dataset consisting of various attributes of prospective students.These attributes extend beyond academic parameters like the grade point average (GPA) from the final high school year, encompassing non-academic data such as parental occupation, income, student's gender, origin province, high school major, and school category.Previous research has predominantly focused on academic and sociodemographic data, such as GPA and family income, respectively, for predicting study performance.However, factors like school major, study program, and school category have often been overlooked.In this study, the objective is to utilize the comprehensive Student Selection Data (SMB) to devise a model for predicting the performance of students in their first semester at Telkom University.The aim is to address the issue of a low rate of on-time graduation by leveraging the untapped potential of SMB data.An Iterative Dichotomiser 3 (ID3) decision tree algorithm forms the backbone of the proposed model, enabling the classification of student performance based on a range of diverse attributes.Information gain-based feature selection revealed the five attributes with the greatest influence on student performance in the first semester: gender, grade point average from the final year of high school, study program, high school major, and school category.These findings underscore the potential of a more inclusive approach to student data analysis in predicting academic success in higher education.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".