Clustering of High-Achieving Students Based on Scores at Junior High School Level Using K-Means Algorithm
Bibliographic record
Abstract
Education plays a crucial role in shaping quality human resources, and student achievement evaluation at the junior high school level is essential for supporting academic guidance, learning programs, and recognition of outstanding students. However, the increasing number of students often makes the process of identifying and categorizing achievement more complex. This study aims to develop a student clustering model at SMP Budi Utomo Binjai using the K-Means algorithm as part of a data mining approach. The input data consisted of 638 student records covering three main variables: average score, counseling score, and extracurricular score. Data were preprocessed and transformed before being processed using MATLAB R2014a, which provides a kmeans() function to automatically group the data into clusters. Several clustering trials were conducted with three to six clusters to evaluate the grouping performance. The results showed that students could be grouped into categories of high, medium, and low achievement, with each cluster having different characteristics of average, counseling, and extracurricular scores. Variance analysis indicated that clusters with smaller variance values represented more compact and homogeneous groupings, while clusters with higher variance values were more heterogeneous. The findings demonstrate that the K-Means algorithm is effective in grouping student performance data objectively, providing useful insights for teachers and school administrators to design more targeted learning strategies, academic interventions, and recognition systems. This research highlights the potential of data mining techniques to support decision-making processes in the education sector.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".