Analyzing Student Performance Using Classification Algorithms and Association Rule Mining.
Bibliographic record
Abstract
Predicting student performance is crucial in the educational sector, as analyzing student status can lead to improve performance. Educational data mining is a research field focused on using real-world online data to improve education systems. This data includes academic, socioeconomic, and demographic details for 524 students, encompassing twenty-two attributes. In this study, the Apriori algorithm was employed for association rule mining to conduct an in-depth analysis of student grades and to explore correlations between foundational professional courses and core professional courses. We compared the performance of classification algorithms such as Quest, Random Forest, and Bayes Network Classifiers. Three classification algorithms were implemented using IBM SPSS Modeler, The study highlighted several factors influencing the accuracy of predictions. Random Forest, which achieved the highest accuracy (89%), was particularly sensitive to features like Internal Assessment Percentage (IAP). Quest, with moderate accuracy, emphasized workload-related features such as Theory And Practical Performance (TNP). In contrast, Bayes Network relied on Attendance (ATD) and a diverse range of features, effectively modeling complex interdependencies but at the cost of increased sensitivity to noise. The Apriori algorithm was applied to mine association rules across all attributes, and the most significant rules were displayed.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".