• School Mathematics
• /
• v.12 no.3
• /
• pp.323-335
• /
• 2010

Linear algebra is not only applied comprehensively in the branches of mathematics such as algebra, analytics, and geometry but also utilized for finding solutions in various fields such as aeronautical engineering, electronics, biology, geology, mechanics and etc. Therefore, linear algebra should be easy and comfortable for not only mathematics majors but also for general students as well. However, most find it difficult to learn linear algebra. Why is it so? It is because many studying linear algebra fail to achieve a correct understanding or attain erroneous concepts through misleading knowledge they already have. Such cases cause learning disability and mistakes. This research suggests more effective method of teaching by analyzing difficulty and errors made in learning system of linear equations.

• Communications of Mathematical Education
• /
• v.21 no.4
• /
• pp.621-646
• /
• 2007

In our new learning environment, we were asked to change our teaching method in our Linear Algebra class. In mathematics class, we could use several math-softwares such as MATHEMATICA, MATLAB, MAPLE, Drive etc.. MATLAB was quite well fit with our Linear Algebra class. In this paper we introduce an efficient way of delivery on important concepts in linear algebra by using well-known MATLAB/ATLAST M-files which we downloded from http://www.umassd.edu/specialprograms/atlast/.

• Communications of Mathematical Education
• /
• v.37 no.1
• /
• pp.65-84
• /
• 2023

The method of Lagrange multipliers, one of the most fundamental algorithms for solving equality constrained optimization problems, has been widely used in basic mathematics for artificial intelligence (AI), linear algebra, optimization theory, and control theory. This method is an important tool that connects calculus and linear algebra. It is actively used in artificial intelligence algorithms including principal component analysis (PCA). Therefore, it is desired that instructors motivate students who first encounter this method in college calculus. In this paper, we provide an integrated perspective for instructors to teach the method of Lagrange multipliers effectively. First, we provide visualization materials and Python-based code, helping to understand the principle of this method. Second, we give a full explanation on the relation between Lagrange multiplier and eigenvalues of a matrix. Third, we give the proof of the first-order optimality condition, which is a fundamental of the method of Lagrange multipliers, and briefly introduce the generalized version of it in optimization. Finally, we give an example of PCA analysis on a real data. These materials can be utilized in class for teaching of the method of Lagrange multipliers.

• Communications of Mathematical Education
• /
• v.18 no.2 s.19
• /
• pp.35-45
• /
• 2004

"2002년 학술진흥재단 대학교육과정개발연구"의 성균관대학교의 9과제 중 하나로 3분야인 "신규교과목 또는 교수학습 방법개발" 과제로 대학에서의 "수학강좌의 효과적인 교수-학습 모델 개발 연구 (선형대수학, 미적분학, 이산수학을 중심으로)" 내용과 그 부산물인 콘텐츠를 소개하고, 이를 효과적으로 이용하기 위하여 개발한 새로운 강의 환경과 교수법을 소개한다. 이어서 현재 국내외에서 활발히 연구가 시작되고 있는 "대학에서의 수학교육" 내용을 소개한 후 대학에서 개발되고 검증된 이런 교수법과 교육환경이 중등학교의 수학 교육 현장에 주는 의미에 대하여 논의한다.

• The Mathematical Education
• /
• v.63 no.2
• /
• pp.123-138
• /
• 2024

This study described the utilization of ChatGPT in teaching and students' learning processes for the course "Introductory Mathematics for Artificial Intelligence (Math4AI)" at 'S' University. We developed a customized ChatGPT and presented a learning model in which students supplement their knowledge of the topic at hand by utilizing this model. More specifically, first, students learn the concepts and questions of the course textbook by themselves. Then, for any question they are unsure of, students may submit any questions (keywords or open problem numbers from the textbook) to our own ChatGPT at https://math4ai.solgitmath.com/ to get help. Notably, we optimized ChatGPT and minimized inaccurate information by fully utilizing various types of data related to the subject, such as textbooks, labs, discussion records, and codes at http://matrix.skku.ac.kr/Math4AI-ChatGPT/. In this model, when students have questions while studying the textbook by themselves, they can ask mathematical concepts, keywords, theorems, examples, and problems in natural language through the ChatGPT interface. Our customized ChatGPT then provides the relevant terms, concepts, and sample answers based on previous students' discussions and/or samples of Python or R code that have been used in the discussion. Furthermore, by providing students with real-time, optimized advice based on their level, we can provide personalized education not only for the Math4AI course, but also for any other courses in college math education. The present study, which incorporates our ChatGPT model into the teaching and learning process in the course, shows promising applicability of AI technology to other college math courses (for instance, calculus, linear algebra, discrete mathematics, engineering mathematics, and basic statistics) and in K-12 math education as well as the Lifespan Learning and Continuing Education.