• Communications of Mathematical Education
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• v.34 no.3
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• pp.215-234
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• 2020

With the advent of the AI, the need to use AI in the field of education is widely recognized. The purpose of this study is to shed light on how prospective mathematics teachers perceive the need for AI and the role of teachers in future mathematics education. As a result, with regard to teaching, prospective teachers recognized that the use of AI in school mathematics is a demand of a new era, that various types of lesson can be implemented, and that accurate knowledge and information can be delivered. On the other hand, they recognized that AI has limitations in having cognitive and emotional interactions with students. As for mathematics learning, the prospective teachers recognized that AI can provide individualized learning, be used for supplementary learning outside of school, and stimulate students' interest in learning. However, they also said that learning through AI could undermine students' ability to think on their own. With regard to assessment, the prospective teachers recognized that AI is objective, fair and can reduce teachers' workload, but they also said that AI has limitations in evaluating students' abilities in constructed-response items and in process-focused assessment. The roles of teachers that the prospective teachers think were to conduct a lesson, emotional interaction, unstructured assessment, and counseling, and those of AI were individualized learning, rote learning, structured assessment, and administrative works.

• Communications of Mathematical Education
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• v.36 no.1
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• pp.107-138
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• 2022

This study is a study that developed class materials that can apply Process-Focused Assessment to classes by paying attention to feedback using teacher learning community programs centered on teachers belonging to the same school in the field. In particular, this study was conducted with the aim of developing class materials applicable to actual classes. At this time, We thought about how to provide appropriate feedback when applying course-based evaluation in school field classes. It was conducted according to the procedure of data development research by Lee & Ahn(2021). As for the procedure of data development itself, an evaluation plan was established by establishing a strategy to reconstruct achievement standards and confirm understanding based on curriculum analysis. Next, an evaluation task, a scoring standard table, and a preliminary feedback preparation table were developed. In addition, based on these development materials, a learning guidance plan that can predict scenes when applying actual classes was developed as a result. This study has value as a practical study that can contribute to providing a link between theory and field schools. It is also meaningful in that it considered how the teacher would grasp when to provide feedback in performing rocess-Focused Assessment. Likewise, in providing feedback by teachers, it is meaningful in that it reflects in the data development how to prepare in advance and take classes according to the characteristics of the subject. Finally, it seems that the possibility of field application can be improved in that the results of the 4th class developed in this study are presented in a form applicable to the class directly in the field.

• Communications of Mathematical Education
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• v.27 no.4
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• pp.523-545
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• 2013

The purposes of the study were to analyze STEAM instructional materials to find research trends on STEAM instructional program and to develop mathematics-centered STEAM instructional program for middle school second graders. To conduct this study, the researcher collected total 123 research papers and thesis focused on the development of STEAM instructional material and deduced implications for the development of mathematics-centered STEAM instrucational materials from the findings. The researcher found that important components of such as 'context for learning', 'internalization-mmersion', 'new challenge', and 'self-assessment' in STEAM education were not reflected properly in 19 mathematics-centered STEAM instructional programs. Therefore, the researcher put more emphasis on those components in the process of developing mathematics-centered STEAM program.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.1
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• pp.1-22
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• 2010

The purpose of this study was to examine the relationship between teacher's role and interaction patterns in mathematics classrooms. Teacher's role was divided into usual practices with students, usual practices with content and usual practices with students and contents, and interaction patterns were classified into report, inquiry and discussion. The subjects in this study were teachers and students in three fourth- grade classes in T elementary school located in Seoul. After the classes of every math teacher were observed, three teachers who played distinctively unique roles were selected in accordance with the results of the first-semester autonomous supervision, of open class for parents and of the instructional observation. Thus, there was a close relationship between the teacher roles and interaction patterns. And it's concluded that students are able to have a more discussion on each other's ideas in the student-centered classroom, and that teachers should perform active roles in that process. Given the findings of the study, there are some suggestions: First, the teachers appeared to fulfill consistent roles when their videotaped classes, study aids and performance assessment materials were analyzed, and they should play more active roles in mathematics class. Second, they should try to create the kinds of climate that encourages students to come up with ideas in an active manner. Third, earlier studies had focused on student-teacher interaction patterns, but this study found that the roles of the teachers depended on interaction with not only students but study aids and performance assessment materials, and that the interaction patterns hinged on their roles as well. Therefore more profound research efforts should be directed into this issue.

• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.