• Communications of Mathematical Education
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• v.36 no.4
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• pp.611-626
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• 2022

In this study, we introduced the case of college calculus course for vocational high school graduates with coding. We suggest this case as an alternative to overcome mathematics anxiety. Contents, python/SageMath codes, and textbook for this course, which help students to easily and quickly review middle and high school mathematics, were newly developed by authors. Due to the use of codes and chat with classmates in learning management system, most of the students who took this course reported that they no longer felt anxious in complex mathematics problems, had a full understanding of calculus concepts, could solve almost problems in any calculus textbooks with or without codes, and could explain calculus concepts to other students in their own words. In this way if mathematics and coding is properly used in mathematics education, it helps students with weak mathematical backgrounds or mathematics anxiety to restore confidence in mathematics in college. This could be applicable in secondary mathematics education.

• Communications of Mathematical Education
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• v.30 no.4
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• pp.515-530
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• 2016

Recently an extensive study of the mathematicians who have overcome the visually impaired and contribute to the academic in math was published. In the case of Korea, we can find there are mathematicians who have overcome physical disabilities such as cerebral palsy and polio. However there is no example of blind person who majored mathematics to become a mathematic's teacher or professor and have entered any mathematics related professions. This let us to study the reasons that caused difficulties to visually impaired students majoring in mathematics. We also suggest ways that may help blind students to have access to mathematics intuitively. In this study, we propose a tactile mathematics textbooks and teaching manuals utilizing 3D printing which the visually impaired students can touch and feel. We can supply such materials to visually impaired youth, special education teachers and parents in Korea. As a result, visually impaired students will be able to access mathematics easily and can build their confidence in mathematics. We hope that some blind students with mathematical talent do not hesitate to major mathematics and choose career in mathematical professions.

• Communications of Mathematical Education
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• v.37 no.2
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• pp.277-297
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• 2023

In mathematics, the concept of convolution is widely used. The convolution operation is required for understanding computer vision and deep learning in artificial intelligence. Therefore, it is vital for this concept to be explained in college mathematics education. In this paper, we present our new teaching and learning materials on convolution available for engineering mathematics. We provide the knowledge and applications on convolution with Python-based code, and introduce Convolutional Neural Network (CNN) used for image classification as an example. These materials can be utilized in class for the teaching of convolution and help students have a good understanding of the related knowledge in artificial intelligence.

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