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