Academic Behavior Analysis and Early Warning System Based on K-Means Algorithm
Bibliographic record
Abstract
This paper presents the design and implementation of an academic behavior analysis and warning system based on the K-Means algorithm. The system combines students' historical grades with current behavioral data to construct a predictive and academic warning model, aimed at assisting educators in quickly identifying academic risks and providing adjustment suggestions for students on the academic edge. The system is divided into registration and login modules, administrator modules, and user modules, realizing functions such as identity authentication, permission allocation, and account management. In the model construction phase, K-Means clustering is applied to training samples, and multiple linear regression models for grade prediction are built based on the clustering results. In the testing phase, grades of experimental groups are predicted and error analysis is conducted. Experimental results show that the system has lower prediction errors in the construction of predictive models for intelligent medical engineering majors and higher prediction accuracy for computer science and technology majors. The system also establishes a four-level warning mechanism, represented by red, orange, yellow, and green, to help users intuitively understand their academic situations. Overall, this study provides effective support for student academic development through a K-Means-based grade prediction system, with practical application value.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".