Abstract

Objectives The purpose of this study is to determine the most effective approach for constructing training data comprising questions and answers for the development of a mathematical question-answering chatbot. This involves a theoretical analysis of mathematical knowledge and an empirical analysis of questions posed by students using the chatbot.
 Methods The study targeted second-year middle school students and created a total of 1,936 pairs of math-related question-answer sets virtually. The chatbot was trained using the Doc2Vec technique, which is based on similarity. During the data construction process, various considerations related to mathematical knowledge and math learning were taken into account to design different types of questions. The data was then used for training the chatbot. Additionally, students utilized the trained chatbot during their classes for seven months, and their input questions were collected and analyzed.
 Results A comparison between the collected dataset of 1,636 questions from 92 middle school students and the types of question-answer pairs used for training the chatbot revealed that conceptual knowledge questions accounted for over 70% of the student-generated data. Students also posed questions to test the chatbot's capabilities and inquire about attitudes and emotional aspects related to mathematics. Within the category of conceptual knowledge questions, deeper inquiries involving comparisons, extensions, and connections of concepts were observed.
 Conclusions The study revealed that when students used the question-answering chatbot, inquiries related to c conceptual understanding of mathematics when students interact with the question-answering chatbot highlights the importance of focusing on conceptual questions. Furthermore, the activity of questioning concepts holds the potential to evolve into more advanced inquiries, fostering connections and extensions of mathematical concepts. Further research is needed to explore more suitable and effective ways of responding to students through the chatbot and utilizing it in educational contexts.

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