• The Mathematical Education
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• v.63 no.3
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• pp.549-571
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• 2024

As digital·AI-based teaching and learning is emphasized, discussions on the educational use of generative AI are becoming more active. This study analyzed the mathematical performance of ChatGPT 4, Claude 3 Opus, and Gemini Advanced on solving examples and problems from five first-year high school math textbooks. As a result of examining the overall correct answer rate and characteristics of each skill for a total of 1,317 questions, ChatGPT 4 had the highest overall correct answer rate of 0.85, followed by Claude 3 Opus at 0.67, and Gemini Advanced at 0.42. By skills, all three models showed high correct answer rates in 'Find functions' and 'Prove', while relatively low correct answer rates in 'Explain' and 'Draw graphs'. In particular, in 'Count', ChatGPT 4 and Claude 3 Opus had a correct answer rate of 1.00, while Gemini Advanced was low at 0.56. Additionally, all models had difficulty in explaining using Venn diagrams and creating images. Based on the research results, teachers should identify the strengths and limitations of each AI model and use them appropriately in class. This study is significant in that it suggested the possibility of use in actual classes by analyzing the mathematical performance of generative AI. It also provided important implications for redefining the role of teachers in mathematics education in the era of artificial intelligence. Further research is needed to develop a cooperative educational model between generative AI and teachers and to study individualized learning plans using AI.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.317-325
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• 2021

In this paper, the degree of reflection of SW·AI elements and CT elements was investigated and analyzed for a total of 44 textbooks of Korean, social, moral, mathematics and science textbooks based on the 2015 revised curriculum. As a result of the analysis, most of the activities of data collection, data analysis, and data presentation, which are ICT elements, were not reflected, and algorithm and programming elements were not reflected among SW·AI content elements, and there were no abstraction, automation, and generalization elements among CT elements. Therefore, in order to effectively implement SW·AI convergence education in elementary school subjects, we will expand ICT utilization activities to SW·AI utilization activities. Training on the understanding of SW·AI convergence education and improvement of teaching and learning methods using SW·AI is needed for teachers. In addition, it is necessary to establish an information curriculum and secure separate class hours for substantial SW·AI education.

• Journal of Elementary Mathematics Education in Korea
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• v.5 no.1
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• pp.99-120
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• 2001

The purpose of this thesis is to find the guiding direction of mathematical communication in lower grade students of elementary school and to present a new direction about the effect of using concrete material in communication. It is expected that mathematical communication increases when concrete material is used for the students of the lower grades, who are in concrete operational period. Therefore, this study ai s to investigate what characteristics there are in mathematical communication of second grade students and what effect concrete materials have on mathematical communication and learning. The analysis of the teaching record shows that the second grade students use alternative terms in the process of communication since they are not familiar with mathematical symbols or terms, which is a characteristic of communication in a mathematics class in which concrete material is used. In the process of teaming the students apply their living experiences to their teaming. Since a small number of students lead class, the interaction between students is also led by them. The direction of communication in a small group is not centered around solution of a problem, and most students show a more interest in finding answers than in the process of learning. The effect that concrete material has on communication plays an important role in promoting students' speaking activity; it allows students to identify and correct their errors more easily. It also makes students' activities more predictable, and it increases a small group activities through the medium of concrete material. However, it was also noticed that students' listening activities are not appropriately developed since they do not pay attention to a teacher who uses concrete material. The effects that concrete material has on mathematics class can be summarized as follows. Concrete material promotes students' participation in class by triggering their interest of learning of mathematics and helps them to understand the course of learning. It also helps the teaming and formation of concepts for children of low academic performance. And it makes a phased learning possible according to students' ability to use concrete material and to solve a problem. Based upon the results above mentioned, the use of concrete material is absolutely needed in mathematics classes of lower grade elementary school students since it increases communication and gives much influence on mathematics learning. Therefore, teachers need to develop teaching or learning method which can help increase communication, considering the characteristics of students' communication.

• The Mathematical Education
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• v.62 no.2
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• pp.289-302
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• 2023

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics. Integrating big data statistical projects with digital technologies in high school <Artificial Intelligence> mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills. This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria. The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning. The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks. In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing. This can potentially result in misconceptions among students regarding the nature of big data. Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks. This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.

• Journal of the Korean Society of Radiology
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• v.14 no.2
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• pp.121-127
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• 2020

The era of the Fourth Industrial Revolution is increasingly demanding mathematical competencies for virtual reality (VR), artificial intelligence (AI) and the like. In this context, this study intended to identify the basic mathematical competency levels of university freshman students in radiology department and to provide basic data thereon. For this, the diagnostic assessment of basic learning competencies for the domain of mathematics was conducted from June 17, 2019 to June 28, 2019 among 78 freshman students of radiology department at S university and D university. As a result, the university students' overall basic mathematical competency levels were diagnosed to be excellent. However, their levels in the sectors of the geometry and vector and the probability and statistics were diagnosed to be moderate, with the mean scores of 2.61 points and 2.64 points, respectively, which were found to be lower than those of the other sections. As for basic mathematical competency levels according to genders, the levels of male students and female students were diagnosed to be excellent, with the mean scores of 17.48 points and 16.29 points, respectively, showing no statistically significant difference (p>0.05). Given the small number of subjects and regional restriction, there might be some limitations in the generalization of the findings of the present study to all university freshman students and all departments. The above results suggest that it is necessary to implement various programs such as student level-based special lectures for enhancing basic mathematical competencies relating to major in order to improve the basic mathematical competencies of freshman students in radiology department, and that it is necessary to increase the students' mathematical competencies by offering major math courses in the curriculum and applying teaching-learning methods matching students' levels.

• The Mathematical Education
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• v.63 no.2
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• pp.165-186
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• 2024

Despite the growing attention on artificial intelligence-based automated scoring technology as a support method for the introduction of descriptive items in school environments and large-scale assessments, there is a noticeable lack of foundational research in mathematics compared to other subjects. This study developed an automated scoring model for two descriptive items in first-year middle school mathematics using the Random Forest algorithm, evaluated its performance, and explored ways to enhance this performance. The accuracy of the final models for the two items was found to be between 0.95 to 1.00 and 0.73 to 0.89, respectively, which is relatively high compared to automated scoring models in other subjects. We discovered that the strategic selection of the number of evaluation categories, taking into account the amount of data, is crucial for the effective development and performance of automated scoring models. Additionally, text preprocessing by mathematics education experts proved effective in improving both the performance and interpretability of the automated scoring model. Selecting a vectorization method that matches the characteristics of the items and data was identified as one way to enhance model performance. Furthermore, we confirmed that oversampling is a useful method to supplement performance in situations where practical limitations hinder balanced data collection. To enhance educational utility, further research is needed on how to utilize feature importance derived from the Random Forest-based automated scoring model to generate useful information for teaching and learning, such as feedback. This study is significant as foundational research in the field of mathematics descriptive automatic scoring, and there is a need for various subsequent studies through close collaboration between AI experts and math education experts.