I am an Assistant Professor in computer science education and educational technology, with an established research background in quantitative methods, including survey-based studies, scale development, and validation related to AI, digital literacy, and computational thinking. My published work includes the development and empirical validation of attitudinal and awareness measures, as well as large-sample analyses examining learners’ and teachers’ perceptions of emerging educational technologies.
Building on this quantitative foundation, my recent research has expanded toward conceptual and qualitative inquiries into how AI systems reshape teachers’ professional judgement, responsibility, and decision-making in practice. Across both strands, my work aims to connect robust measurement and empirical evidence with theoretically grounded interpretations of human–AI interaction in educational contexts.
I am particularly interested in international collaborations that integrate quantitative instruments, cross-cultural validation, and practice-based analysis to advance theory-informed research in AI-supported education.
- Quantitative research methods in education (survey design scale development validation) - Attitudes awareness and perceptions toward AI and educational technology - Artificial Intelligence in Education (AIEd) and educational technology adoption - Computational thinking and digital literacy in K–12 and teacher education - Cross-cultural and comparative studies using validated measurement instruments - Teachers’ professional judgement and responsibility in AI-supported contexts - Human–AI interaction cognitive offloading and boundary-setting in education - Conceptual and mixed-method approaches to AI ethics and educational practice
Due to COVID-19, numerous new technologies are being implemented in education, with a growing interest in the metaverse. The term “metaverse” refers to an immersive digital environment where one can interact with virtual avatars. This study aims to analyze the experiences and attitudes of the metaverse for learner-centered education from a constructivist perspective to determine how closely related this virtual environment is to the lives of elementary school students. This study also examined how students are becoming the focal point of new educational technologies. After reviewing the literature on this topic, a survey of 336 elementary school students in Korea was conducted using 18 items for measuring each factor in the metaverse, followed by statistical analyses that included a difference of means and an independent sample t-test. The results revealed that, on average, 97.9% of elementary school students had experiences with the metaverse, with 95.5% of them considering it closely related to their everyday life. In addition, various conclusions according to each metaverse factor and each participant’s gender are provided.
Artificial intelligence (AI) education is becoming increasingly important worldwide. However, there has been no measuring instrument for diagnosing the students’ current perspective. Thus the aim of this study was to develop an instrument that measures student attitudes toward AI. The instrument was developed by verifying the reliability and validity by 8 computer education PhD using a sample of 305 K-12 students. This scale made students’ attitudes toward AI operational and quantifiable. Accordingly, educators can use it to diagnose the current status of students or verify the effectiveness of new AI education methods.
2019년부터 소프트웨어 교육은 모든 초등학생들이 배워야 하는 필수과목이 되었다. 하지만 아직 어떤 식으로 수업을 진행해야 할지에 대해서는 많은 교사들이 낯설어하고 있다. 이에 이 논문에서는 소프트웨어 교육의 핵심 이 되는 컴퓨팅 사고력 중 데이터 수집, 분석, 표현 수업에 도움이 되고자 각각의 의미, 세부역량과 성취기준을 제시하고 예시수업을 통해 적용 가능성을 제시하였다. 논문의 전체 과정을 요약하면 다음과 같다. 첫째, 기존의 연구들에서 데이터 관련 역량의 의미, 세부역량 및 성취기준에 관한 연구들을 정리하고 이것을 바탕으로 하여 예비조사를 진행하였다. 예비조사에서는 FGI와 폐쇄형 질문을 동시에 진행하였으며 이를 통하여 전문가들의 검 토의견을 반영한 본 조사의 설문 문항을 작성하였다. 둘째, 위의 결과로 만들어진 설문문항을 컴퓨터교육 전공 박사, 박사과정, 소프트웨어교육 담당 교사 및 소프트웨어교육 종사자를 대상으로 하여 타당도, 안정도, 신뢰도를 검증받았다. 셋째, 그 결과 개발된 세부역량과 성취기준 중 ‘수집방법 선택-문제 상황에 따라 수집 방법을 선택 할 수 있다’, ‘데이터의 의미 탐색-분석된 데이터들이 어떤 의미를 갖는지 안다.’, ‘다양한 표현방법 활용-다양한 표현 도구를 사용한다.’를 수업목표로 하여 다섯 차시의 수업을 개발하여 적용하였으며 이 때 교육, 수업, 평가의 일체화를 위해 백워드 2.0 설계모형을 활용하였다. 그 결과 최종적으로 데이터 수집, 분석, 표현의 세부역량과 성 취기준을 제시하였다. 이는 초등학교에서 데이터 관련 수업을 계획함에 있어 어떠한 방향으로 수업을 하면 좋을 지에 대한 구체적이고 명확한 기준을 세울 때 도움이 될 수 있을 것이다.
Objectives In this study, we aimed to verify the effects of the learning psychological characteristics and digital literacy education activities of middle and high school students in Gyeonggi Province on digital literacy and students' core competencies through structural equation modeling. Methods To this end, we utilized data from the ‘Gyeonggi Province School Education Status Survey’ conducted in 2021 and 2022 by the Gyeonggi Educational Research Institute. The analysis focused on the responses from 8,884 middle school students in the first year (2021) and 4,981 high school students in the second year (2022), examining their psychological characteristics, digital literacy education activities, digital literacy, and core competencies. Structural equation modeling was conducted using R software to explore the relationships among the variables, mediating effects, and pathways. Results The results of this study indicated significant influences among learning psychological characteristics, digital literacy education activities, digital literacy, and core competencies of students. Additionally, when comparing pathways between middle and high school students, it was found that the influence of digital literacy on core competencies, the impact of digital literacy education activities on digital literacy, and the effect of digital literacy education activities on core competencies were lower for middle school students than for high school students. However, the influence of learning psychological characteristics on digital literacy was significantly higher for middle school students compared to high school students. Conclusions Learning psychological characteristics, digital literacy education activities, and digital literacy significantly influence students' core competencies. This suggests that various educational approaches focusing on learning psychological characteristics and digital literacy can play a crucial role in enhancing the core competencies of future learners.
Journal of the International Network for Korean Language and Culture 22-2, 1-26. This study explored a way for analyzing Korean writing learners’ attitudes toward AI use based on data science. It focused on developing scale items to measure Korean learners' attitudes toward AI use at each stage of the writing process. This study first examined trends in existing discussions on AI in Korean writing education. It identified a lack of systematic research on learners’ attitudes within the prevailing discourse, which is broadly divided into the perception and use of AI. Subsequently, the study reviewed analytical methods to determine attitudes toward AI based on data science, confirming cognitive, affective, and behavioral components as key elements of attitude. Accordingly, this study developed an attitude scale for each stage of the Korean-writing process, specifically proposing scale items reflecting cognitive, affective, and behavioral elements across the stages of planning, generating, organizing, expressing, and revising. (Gyeongkuk National University)
