Abstract
performance prediction is a great area of concern for educational institutions to prevent their students from failure by providing necessary support and counseling to complete their degree successfully. The scope of this research is to examine the accuracy of the ensemble techniques for predicting the student's academic performance, particularly for four year engineering graduate program. To this end, five ensemble techniques based on four representative learning algorithms, namely Adaboost, Bagging, Random Forest and Rotation Forest have been used to construct and combine different number of ensembles. These four algorithms have been compared for the same number (ten) of base classifiers and the Rotation Forest is found to be the best ensemble classifiers for predicting the student performance at the initial stages of the degree program.
Published Version (
Free)
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 callDisclaimer: 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.
