Jeong-su Ahn , Min-kyeong Ji , Yeonseok Ka , Ki-jeong Bae , Seung-jin Cha , Jihyun Hwang

DOI: Vol.27(No.4) 431-447, 2024

Secondary analysis of existing data provides unique opportunities for researchers to conduct large-scale studies with enhanced efficiency of resources and time, a concept increasingly utilized in educational research. This study aimed to provide crucial insights into the alignment between original and secondary theoretical frameworks, the creation and interpretation of variables, and the careful handling of data structures. This study explored the specific challenges and opportunities of using secondary analysis through two illustrative cases involving the Trends in International Mathematics and Science Study (TIMSS) and the Seoul Educational Longitudinal Study (SELS). The case with the TIMSS data highlighted the complexities of applying theoretical frameworks and interpreting newly generated variables when the original data lacked certain measurements needed for current research questions. A significant challenge identified in the SELS data was the non-uniformity of ID structures over years, which complicates the understanding of longitudinal trends and demands meticulous attention to detail. Our cases underscored the necessity for rigorous theoretical alignment, precise operational definitions, and nuanced statistical interpretations when conducting secondary analysis and international comparison. These are essential to unlock the full potential of large-scale public datasets in educational research, ensuring that findings are both scientifically robust and practically relevant.

secondary analysis, longitudinal studies, theoretical alignment, TIMSS, SELS

Sang-gu Lee , Jae Hwa Lee , Dong Sun Lim , Doyoung Park

DOI: Vol.27(No.4) 449-466, 2024

Recently, Lee et al. (2024) discussed the utilization of a course-tailored ChatGPT in the teaching and learning process of the college-level course, ‘Introductory Mathematics for Artificial Intelligence (Math4AI).’ They suggested a methodology for applying AI, including ChatGPT, to various mathematical subjects and addressed current issues, such as personalized learning aids for students, cost reduction in tutoring, and alleviating educators’ workload. GPT-4o, which is capable of real-time processing of voice, text, images, and videos, offers human-like interactions and personalized explanations tailored to each student’s level of understanding. These features make it a promising tool for mathematics education. This study builds on Lee et al. (2024) by investigating the impacts of GPT-4o on college-level mathematics education. We employed a process-focused assessment method with a customized ChatGPT-based educational platform. By incorporating methods such as open-ended problem-solving, Q&A discussions, problem/project-based learning (PBL) reporting, presentations, and peer evaluations, we observed a significant improvement in students' engagement and comprehension, demonstrating the model's effectiveness in the integration of Artificial Intelligence (AI) in mathematics education. These results offer a pathway for individualized learning and efficient instructor assessment.

Artificial Intelligence, AI, ChatGPT, college mathematics education, mathematics for AI, Math4AI, GPT-4o

Sunyoung Han , Seonyoung Hwang

DOI: Vol.27(No.4) 467-494, 2024

Pre-service mathematics teachers who entered the College of Education experienced a disconnection between higher mathematics and school mathematics. Moreover, in-service mathematics teachers also experienced the disconnections in teaching school mathematics after graduating the university. These disconnections have been recognized as an issue for a very long time since Klein called it as double discontinuity in 1908. This study attempted a pedagogical approach to overcome the double discontinuity problem of pre-service mathematics teachers. According to the results of this study, pre-service mathematics teachers basically tried didactic transpositions based on textbooks in the pre-assignment. After the project, pre-service mathematics teachers attempted to make additional didactic transpositions based on the differences they recognized through project activities comparing the content between university mathematics and school mathematics. In addition, through teachers' impressions, it was confirmed that this project helped resolve the double disconnection that teachers were experiencing. Implications based on the findings were discussed.

algebra, quadratic equation, collective case study, college mathematics

Jihyun Hwang , Yeonseok Ka , Jinho Kim , Na Yong Kwon

DOI: Vol.27(No.4) 495-516, 2024

This study explores the professional development of pre-service mathematics teachers by analyzing their written work, with a focus on how their perspectives evolved over the course of a semester. Writing plays a critical role in mathematics education, fostering reflective practice, improving communication, and deepening conceptual understanding. Using latent Dirichlet allocation (LDA), a topic modeling technique, this research examined a collection of writings from pre-service teachers enrolled in a constructivist-focused mathematics education course. The analysis identified key topics that emerged, changes in the prominence of these topics over time, and unique characteristics distinguishing the writing of pre-service mathematics teachers from their peers in other disciplines. The findings revealed that writing effectively captured the shifting priorities of pre-service teachers, such as their emphasis on teacher roles and learner-centered instruction. Variability in topic focus throughout the semester may reflect the influence of a changing educational context, particularly in remote or hybrid learning settings. These dynamic shifts underscore the interplay between the pre-service teachers' growing understanding and their evolving learning environments. This study contributes to the expanding literature on pre-service teacher development by demonstrating how machine learning techniques can complement traditional qualitative methods, offering deeper insights into their professional growth. The results suggest that integrating writing analysis into teacher education programs can enhance support for reflective practice and adaptive teaching strategies.

pre-service mathematics teachers, writing in mathematics education, topic modeling, latent Dirichlet allocation

Young-ok Kim

DOI: Vol.27(No.4) 517-535, 2024

This study investigates the challenges preservice mathematics teachers face in interpreting and applying the achievement standards of the 2022 Revised Mathematics Curriculum. The research focuses on the integration of content elements, such as "Knowledge & Understanding," "Processes & Skills," and "Values & Attitudes," in achievement standards and their implications for teaching practices. Through team-based projects, preservice teachers analyzed achievement standards, their explanations, and application considerations, identifying specific difficulties in designing lesson plans that align with these elements. The findings reveal that achievement standards combining all three content elements posed the greatest challenges, particularly in translating affective objectives into practical teaching strategies. The study emphasizes the need for teacher education programs that enhance curriculum literacy and provide structured guidance to support preservice teachers in aligning their instructional practices with curriculum objectives. These insights aim to inform curriculum developers, teacher educators, and policymakers to bridge the gap between curriculum design and classroom implementation.

2022 revised mathematics curriculum, achievement standards, curriculum interpretation, content elements

Kyunghoon Son , Lillie R. Albert

DOI: Vol.27(No.4) 537-563, 2024

The purpose of this study is to examine the effect of students’ sense of belonging in school on their mathematics performance. Numerous reports have consistently highlighted the significant impact of students’ emotional states on their learning. Thus, creating an environment where students can study in a stable and supportive atmosphere is essential not only for their mental health but also for their mathematics achievement. Using path analysis modeling on the 2019 South Korean 8th-grade dataset from the Trends in International Mathematics and Science Study, this study examined the influence of students’ sense of belonging in school on their affective domains-such as their perception of teachers and the orderliness of the mathematics classroom atmosphere-and their mathematical achievement. The results revealed that students’ sense of belonging, when mediated by their perception of their teacher, positively influenced mathematics achievement. However, when mediated by their perception of the orderliness of the mathematics classroom atmosphere, students’ sense of belonging had a negative impact on mathematics achievement. The findings highlight both the implications and limitations of the study, emphasizing the need for further exploration of these complex relationships.

sense of belonging, students’ teacher perception, classroom atmosphere, path analysis modeling