A Review on Predictive Modeling Technique for Student Academic Performance Monitoring

• Main Authors: Asiah, Mat, Nik Zulkarnaen, Khidzir, Safaai, Deris, Nik Nurul Hafzan, Mat Yaacob, Mohd Saberi, Mohamad, Siti Syuhaida, Safaai
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Analytics; Clustering; Decision analysis; Decision making; Learning; Modelling; Performance prediction; Regression analysis; Students
• ISSN: 2261-236X; 2274-7214; 2261-236X
• DOI: 10.1051/matecconf/201925503004

Despite of providing high quality of education, demand on predicting student academic performance become more critical to improve the quality and assisting students to achieve a great performance in their studies. The lack of existing an efficiency and accurate prediction model is one of the major issues. Predictive analytics can provide institution with intuitive and better decision making. The objective of this paper is to review current research activities related to academic analytics focusing on predicting student academic performance. Various methods have been proposed by previous researchers to develop the best performance model using variety of students data, techniques, algorithms and tools. Predictive modeling used in predicting student performance are related to several learning tasks such as classification, regression and clustering. To achieve best prediction model, a lot of variables have been chosen and tested to find most influential attributes to perform prediction. Accurate performance prediction will be helpful in order to provide guidance in learning process that will benefit to students in avoiding poor scores. The predictive model furthermore can help instructor to forecast course completion including student final grade which are directly correlated to student performance success. To harvest an effective predictive model, it requires a good input data and variables, suitable predictive method as well as powerful and robust prediction model.