Communications of Mathematical Education

Issue: Vol.34 No.1

Artificial Intelligence and College Mathematics Education 이상구 Lee Sang-gu , 이재화 Lee Jae Hwa , 함윤미 Ham Yoonmee 34(1) 1-15, 2020 Artificial Intelligence and College Mathematics Education 이상구 Lee Sang-gu , 이재화 Lee Jae Hwa , 함윤미 Ham Yoonmee DOI: JANT Vol.34(No.1) 1-15, 2020 Today’s healthcare, intelligent robots, smart home systems, and car sharing are already innovating with cutting-edge information and communication technologies such as Artificial Intelligence (AI), the Internet of Things, the Internet of Intelligent Things, and Big data. It is deeply affecting our lives. In the factory, robots have been working for humans more than several decades (FA, OA), AI doctors are also working in hospitals (Dr. Watson), AI speakers (Giga Genie) and AI assistants (Siri, Bixby, Google Assistant) are working to improve Natural Language Process. Now, in order to understand AI, knowledge of mathematics becomes essential, not a choice. Thus, mathematicians have been given a role in explaining such mathematics that make these things possible behind AI. Therefore, the authors wrote a textbook ‘Basic Mathematics for Artificial Intelligence’ by arranging the mathematics concepts and tools needed to understand AI and machine learning in one or two semesters, and organized lectures for undergraduate and graduate students of various majors to explore careers in artificial intelligence. In this paper, we share our experience of conducting this class with the full contents in http://matrix.skku.ac.kr/math4ai/.
Analysis of Belief Types in Mathematics Teachers and their Students by Latent Class Analysis 강성권 Kang Sung Kwon , 홍진곤 Hong Jin-kon 34(1) 17-39, 2020 Analysis of Belief Types in Mathematics Teachers and their Students by Latent Class Analysis 강성권 Kang Sung Kwon , 홍진곤 Hong Jin-kon DOI: JANT Vol.34(No.1) 17-39, 2020 The purpose of this study is to analyze the mathematical beliefs of students and teachers by Latent Class Analysis(LCA). This study surveyed 60 teachers about beliefs of 'nature of mathematics','mathematic teaching','mathematical ability' and also asked 1850 students about beliefs of ‘school mathematics’,‘mathematic problem solving’, ‘mathematic learning’ and ‘mathematical self-concept’. Also, this study classified each student and teacher into a class that are in a similar response, analyzed the belief systems and built a profile of the classes. As a result, teachers were classified into three types of belief classes about 'nature of mathematics' and two types of belief classes about 'teaching mathematics' and 'mathematical ability' respectively. Also, students were classfied into three types of belief classes about 'self concept' and two types of classes about 'School Mathematics', 'Mathematics Problem Solving' and 'Mathematics Learning' respectively. This study classified the mathematics belief systems in which students were categorized into 9 categories and teachers into 7 categories by LCA. The belief categories analyzed through these inductive observations were found to have statistical validity. The latent class analysis(LCA) used in this study is a new way of inductively categorizing the mathematical beliefs of teachers and students. The belief analysis method(LCA) used in this study may be the basis for statistically analyzing the relationship between teachers' and students' beliefs.