Grouping of Student Learning Interest Indicators using the Clustering Method (Case Study: MA. Al – Asy'ariah Sunggal)
Bibliographic record
Abstract
Student learning interest is an important factor in achieving educational outcomes, as it is directly related to their involvement in the learning process. However, in reality, each student has a different character and learning style, often making it difficult for teachers to determine effective and appropriate learning strategies. Therefore, an approach that can objectively identify patterns of student learning interest is needed. This study aims to group students based on three main indicators of learning interest: class activity, academic grades, and involvement in extracurricular activities. The method used is K-Means Clustering, which is a data mining technique for grouping data based on similar characteristics between objects. This research process began with data collection of 508 students of MA Al- Asy'ariah Sunggal in the 2024 academic year, then the data was transformed into numeric form. Next, the K-Means algorithm was implemented using MATLAB R2014b software. The analysis results show that students can be divided into three main clusters, each with different learning interest characteristics. The first cluster consists of students who are less active and do not participate in extracurricular activities, the second cluster contains students with high academic grades but minimal classroom engagement, and the third cluster reflects students who are active both academically and non-academically. These results provide a concrete picture for schools in developing more targeted learning strategies, based on the needs and potential of students in each group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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 | 0.000 |
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".