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

Arlindo Almada, Qicheng Yu, Preeti Patel
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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.
将NLP K-Means算法整合到主动聊天机器人中以帮助不及格的学生
对大多数大学来说,预测失败并个别帮助失败的学生是一项持续的挑战。本文主要研究了应用于主动聊天机器人的k-means算法的自然语言处理和聚类。它旨在帮助学生,特别是识别和预测不及格的学生,并主动帮助他们。在此基础上,对影响学生学业成绩的可控因素进行干预。首先,作者概述了实现这一目标的研究背景,并创建了一个学生学习成绩的预测模型。研究结果表明,各变量之间的相关性为0.935,精密度为0.76。其次,使用k-means算法对学生的问题或影响学生学习成绩的不同因素进行聚类。最后,将k-means算法集成到一个主动聊天机器人中,根据学生的问题组来帮助他们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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