DATA COLLECTION TO IDENTIFY STUDENTS AT RISK OF NOTCOMPLETING A COURSE USING MACHINE LEARNING

Abstract

One of the most important methods in the study of various subjects is the understanding at an early stage of the learning process on the part
of both the teacher and the student that the student is in a risk group that will not complete the course successfully. Identifying this group of students at an early stage of learning increases the level of motivation of students to start studying well in time and can help the teacher individually determine which student needs help. Before identifying a group of students at risk of not completing the course successfully, an important part is to collect and prepare the necessary data (predictors) for teaching machine learning algorithms. Currently, this is necessary for both online and offline education. In the presented method of determining a group of students, various types of algorithms were used, where one of the best results of determining a group of students with risk and without risk was shown by Logistic Regression with a high AUC =0.8003. The SMOTE method was used in the work, which coped well with the problem of data imbalance of the "Pass" and "Not Pass" classes, while increasing the accuracy of the forecast for the minority class "Not Pass" by 11%. Using certain predictors of student performance, it is possible to derive additional information such as the level of interest in the lesson, the determination of the final score for the lesson, a certain category (A, B, C, D) of students with different characteristics and other indicators that contribute to the involvement of students in the lesson at the earliest stage of learning.