Analysis of Students' Misconducts in Higher Education using Decision Tree and ANN Algorithms
Abstract
A major problem that the Higher Education Institutions (HEIs) face is the misconduct of students’ behavior. The objective of this study is to decrease these misconducts by identifying the factors which cause them on college campuses. CRISP-DM Methodology has been applied to manage the process of data mining and two data mining techniques: J48 Decision Tree (DT) and Artificial Neural Networks (ANNs) have been used to build classification models and to generate rules to classify and predict students' behavior and the location of misconduct in college campuses. They take into consideration seven factors: Student Major, Student Level, Gender, GPA Cumulative, Local Address, Ethnicity, and time of misconduct by month. Both techniques were evaluated and compared. The accuracy results were high for both classification models, whereas the J48 Decision Tree gave higher accuracy.
Keywords:
J48 decision tree, artificial neural networks, machine learning, student misconductDownloads
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