NLP K-Means Algorithm Incorporated into a Proactive Chatbot to Assist Failing Students

Abstract

Predicting failure and individually assisting failing students is an ongoing challenge for most universities. This paper focuses on natural language processing and clustering the k-means algorithm applied to active chatbots. It aims to help students, and specifically to identify and predict failing students and proactively help them. Furthermore, it suggests an intervention to help students based on controllable academic factors that affect their academic performance. First, the authors outlined the research context for achieving this goal and created a predictive model of students' academic performance. The research results indicate a correlation between the variables with an accuracy of 0.935 and a precision of 0.76. Next, the k-means algorithm was used to cluster the students' problems or different factors that affect the students' academic performance. Finally, the k-means algorithm was integrated into an active chatbot to help students according to their problem groups.