Optimization of Educational Teaching Resources in Colleges and Universities Based on Decision Tree Algorithm

Abstract

This paper presents a comprehensive approach to optimizing educational teaching resources in colleges and universities through the application of decision tree algorithms. Drawing from machine learning methodologies, particularly decision tree modeling, we propose a systematic framework for enhancing resource allocation, teaching effectiveness, and student outcomes. The methodology encompasses data collection and preprocessing, feature selection, model training, evaluation, and validation. Key aspects include the identification of pertinent features influencing resource optimization, such as student demographics, course characteristics, faculty qualifications, and infrastructure availability. Through iterative refinement and ethical considerations, our approach aims to transparently and equitably enhance the efficiency and effectiveness of educational teaching resources. By implementing this framework, institutions can make informed decisions to improve student learning experiences and academic performance.