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Arlindo, Almada, Yu, Qicheng and Patel, Preeti (2023) NLP K-means algorithm incorporated into a proactive chatbot to assist failing student. In: International Conference on Intelligent Computing and Machine Learning, 14-16 April 2023, China (Virtual).
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 outline the research context for achieving this goal and created a predictive model of students’ academic performance. The results of the study showed a correlation between the variables and an accuracy of 0.935, and a precision of 0.76 were achieved. 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.
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