Design of Student Behavior Prediction and Management Strategies Supported by Cluster Computing
Bibliographic record
Abstract
In recent years, the deepening of reform and opening up, the deepening of the socialization of college management, the trend of students' thinking is more and more diversified leading to the frequent occurrence of college students' behavior.This paper is based on Spark's parallel H-mine cluster computing to mine the behavioral characteristics data of students in frequent item sets.Using the K-Means clustering algorithm optimized by information entropy and density, the clustering and classification process is carried out according to the central value of the obtained behavioral features.Construct the class model of student behavioral features, realize student behavior prediction by K-nearest neighbor algorithm, and build the early warning model of student behavior prediction based on Spark cluster.The results of clustering analysis show that the average number of times a class of students, the second class of students, and the third class of students eat at breakfast is 120.07,107.66, and 118.25, respectively, and the first class of students has the most number of times of breakfast meals, which shows that this class of students has better eating habits.The number of students studying on March 24, 2023 is predicted by the model based on the K nearest neighbor algorithm, and the trajectory of the real value and the predicted value The number of students with relative error less than 0.2 accounted for 86.42%, indicating that the model is good at predicting the number of students as a whole.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".