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.
Highlights
Higher Education Institutions (HEIs) constitute an environment in which students, families, educators and community members have opportunities to learn, teach, and grow
HEIs face problems which impede the educational process, problems related with students' prohibited behavior which results in dangerous misconducts in the short and the long term
There many studies describing the danger of problematic behaviors and different solutions have been suggested to decrease the number of misconducts that affect negatively the environment of HEIs' and its safety, but very limited studies have used Data Mining (DM) methodologies and techniques in this area
Summary
Higher Education Institutions (HEIs) constitute an environment in which students, families, educators and community members have opportunities to learn, teach, and grow. Educators do their best to provide students with a stable and positive learning environment. Many HEIs' departments and local school districts have taken steps to decrease these behaviors and support all the developed ways and enhance the educational process to keep the educational environment secure and safe for all students. There many studies describing the danger of problematic behaviors and different solutions have been suggested to decrease the number of misconducts that affect negatively the environment of HEIs' and its safety, but very limited studies have used Data Mining (DM) methodologies and techniques in this area
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Engineering, Technology & Applied Science Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
