Demystifying help-seeking students interacting multimodal learning environment under machine learning regime

Help-seeking students are those who seek academic help during the assignment or course. Classifying help-seeking students in a virtual learning environment (VLE) is a challenging task for the instructor because the student is not physically present. In this study, machine learning techniques and statistical methods were used to detect the help-seeking student by analyzing the student logs data in an e-learning system. We determined that which factors are associated with help-seeking behavior of the students. We found that late submitted, and low assessment score students need more help in solving the course assignment. Also, the result shows that Decision Tree (DT), and Fast Large Margin (FLM) is high accuracy predictive machine learning models as compared to Support Vector Machine (SVM), and Logistic Regression (LR) finding the help-seeking students in a course and instructors can easily categorize the students who seek help, disseminate personalized feedback to those students accordingly, and also embrace the sustainable environment for education.