Comparative Prediction of Student Course Preference Using DT-KNN Hybrid Model
Year: 2025
Type: conference-paper
Abstract: This study aims to predict students' course preference levels (categorized into three levels: high, medium, and low) using Decision Tree (DT) and K-Nearest Neighbors (KNN) algorithms by analyzing academic performance, attendance rates, classroom participation, and regular assignment data. Based on the analytical results, targeted teaching optimization strategies will be proposed. Distinct datasets were employed to train DT and KNN classification models respectively, with systematic evaluation and comparison of their predictive performances. The research demonstrates that the DT algorithm exhibits superior practical value in course preference prediction, with the experimental model achieving an accuracy rate exceeding 90%.
Authors: Ruikai Chen, Ruichun Chen
Institutions: City College of Huizhou, Guangzhou Huanan Business College
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4412789878