A Novel Approach to Responding to Key Data Anomalies Using Unsupervised Learning: Work In Progress
Bibliographic record
Abstract
The Student Information System (SIS) plays a crucial role in managing educational data, supporting policy-making, planning, and budget allocation, including the School Operational Assistance (BOS) program. However, data anomalies such as duplicate identities, entry errors, and administrative inconsistencies undermine data validity. Manual anomaly handling is inefficient, particularly at a national scale. This study proposes an unsupervised machine learning approach using the One-Class Support Vector Machine (OC-SVM) to automatically and continuously detect anomalies in educational datasets. A total of 25,204 records from Bengkulu Province were used, with preprocessing involving cleaning, encoding, scaling, and data splitting. Model training includes hyperparameter tuning for optimal performance. Evaluation is conducted using accuracy, precision, recall, F1-score, and confusion matrix. Experimental results show that the OC-SVM model successfully detected 14,424 anomalies with an accuracy of 95%. These findings highlight the potential of automated approaches to improve the quality of national educational data and support future integration into SIS dashboards and further development through ensemble methods.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".