Application of Decision Tree Algorithm in Teaching and Learning Management in Colleges and Universities

Abstract

In modern educational settings, effective decision-making plays a crucial role in ensuring student success and fostering academic excellence. The decision tree algorithm is a valuable tool in teaching and learning management in colleges and universities, offering insights that inform strategic decision-making processes. By analyzing student data, including academic performance, engagement metrics, and demographic information, decision tree algorithms can identify patterns and trends that help optimize educational practices. These algorithms aid in course planning, student advising, and resource allocation by predicting student outcomes, identifying at-risk students, and recommending personalized interventions. This paper explores the application and impact of the Decision Tree Wrapper Sampling Class Imbalance Knowledge Assimilation (DT-WSCIKA) method in enhancing educational decision-making processes. Through a comprehensive analysis of teaching and learning aspects, classification performance metrics, and student performance outcomes across various scenarios, we investigate the efficacy of DT-WSCIKA in addressing class imbalances, optimizing decision-making, and improving educational outcomes. The findings reveal significant improvements in key metrics following the implementation of DT-WSCIKA. For example, classification accuracy increased by an average of 8%, precision improved by 10%, recall by 6%, F1 Score by 7%, and ROC AUC by 9% over multiple epochs. Additionally, student performance outcomes showed an average increase of 7.5% in Scenario 1, 6.2% in Scenario 3, and 8.3% in Scenario 7 when DT-WSCIKA was employed.