Kyong Mi Choi

DOI:10.63311/jksmed.2026.29.2.53 Vol.29(No.2) 53-54, 2026

David Kirshner

DOI:10.63311/jksmed.2026.29.2.55 Vol.29(No.2) 55-69, 2026

The incommensurability of theoretical frameworks has long been recognized as a challenge for coherence in framing of educational practice (e.g., Cobb, 1994; Sfard, 1998). I review four solutions that have arisen over the past 100 years, all seeking to impose a single theory on the field of education, or else to combine independent theories. I argue that these approaches reflect an imperative within psychology toward paradigmatic unity as needed for that science to advance from the adolescent or “preparadigm” phase to scientific maturity (Kuhn, 1970). Instead, I advance the Genres Strategy that provides special purpose pedagogical methods toward learning of skills, concepts, and cultural practices, respectively, each informed by an independent theorization of learning.

theory, practice gap, educational theory, learning theory, holistic education, scientific paradigms, educational efficacy

Yun Sun Hwang

DOI:10.63311/jksmed.2026.29.2.71 Vol.29(No.2) 71-95, 2026

This study proposes and theoretically structures the Behavior-Based Evaluation Mechanism (BBEM) as an assessment framework for organizing and sustaining learners’ behavioral participation in cognitively demanding learning environments. University mathematics learning, particularly proof-oriented instruction, requires sustained participation, procedural responsibility, and continuous engagement in addition to cognitive competence. However, existing assessment research has primarily focused on measuring academic achievement, providing feedback, and supporting self-regulated learning, with relatively limited attention to assessment as a mechanism for structurally organizing learner participation and maintaining instructional stability. Drawing on self-regulated learning theory, sociocultural learning theory, formative assessment, assessment as learning, and sustainable assessment, this study reconceptualizes assessment as a structural regulatory mechanism for behavioral participation. BBEM is conceptualized as a cyclical regulatory mechanism consisting of norm clarification, feedback and intervention, and self-reflection. In some implementations, BBEM may also incorporate threshold-based regulation to prevent a regulatory vacuum in which learners disengage after exhausting participation-based evaluation points. BBEM is not a punitive checklist or a fixed penalty system, but a context-adaptive assessment mechanism that can be differently implemented depending on instructional goals, learner characteristics, and educational contexts. By theoretically structuring practitioner-grounded experiences from university teaching contexts, this study extends the role of assessment beyond achievement measurement and proposes a framework for supporting learner participation and the operational stability of cognitively demanding learning environments. The proposed framework offers a theoretical foundation for exploring broader applications of assessment in higher mathematics education and other learning contexts requiring sustained participation and self-regulation.

Behavior-Based Evaluation Mechanism (BBEM), cognitively demanding learning, behavioral participation, self-regulated learning, assessment reconceptualization, university mathematics education

Jeongwon Kim

DOI:10.63311/jksmed.2026.29.2.97 Vol.29(No.2) 97-120, 2026

As the importance of artificial intelligence (AI) is growing in diverse areas, the need for incorporating AI into education through K-12 grade levels is increasing. The successful implementation of AI in education requires the proper preparation of teachers who practically drive AI systems in the instructional process. In this study, we have conducted a questionnaire survey and utilized quantitative methodology using multigroup latent profile analysis (LPA) to scrutinize teachers’ perceptions of using AI for teaching, learning, and assessing mathematics. A total of 318 Korean teachers from two sample groups, 161 elementary school teachers and 157 secondary mathematics teachers, participated in the study. The results of multigroup LPA identified three distinct latent profiles for elementary and secondary mathematics teachers respectively, and revealed differences in mean scores, variance, and profile size between the two sample groups. Additionally, multinomial logistic regression demonstrated that teachers' AI readiness was associated with an increased likelihood of being categorized into profiles that are more receptive to AI integration. Based on the results, we discussed the pedagogical implications for teachers to effectively use AI in mathematics education.

teacher perceptions, artificial intelligence, mathematics education, elementary school teacher, secondary school teacher