Student Academic Performance Assessment Using Machine Learning – A Literature Review

Abstract

For academic performance assessment of student, traditional methods like written exams and other manual evaluations fail to capture the student’s academic performance. In professional programs like Master of Computer Applications (MCA), where students should have proficiency in various fields like coding, designing, analyzing , soft skills etc. Hence traditional assessment methods are less efficient to capture various influencing factors. Therefore by integrating academic as well as behavioural data of students, one can identifycritical performance parameters needed for performance assessment using machine learning techniques like descriptive analytics and feature selection techniques. Various Machine Learning algorithms can also be used for comparative analysis of the same. It will be helpful for proper decision making for a student in his/her own career path.