The purpose of this study is to develop a comprehensive classification system for discussion-based instruction applicable to elementary, middle, and high schools as well as higher education, and to examine its validity through a multi-stage verificati...
The purpose of this study is to develop a comprehensive classification system for discussion-based instruction applicable to elementary, middle, and high schools as well as higher education, and to examine its validity through a multi-stage verification process. To this end, an extensive literature review was conducted to analyze existing classification frameworks and theoretical foundations related to discussion-based learning. In order to strengthen contextual relevance and practical applicability, focus group interviews(FGI) were carried out with in-service teachers and domain experts. Subsequently, a Delphi survey was administered to validate the proposed classification criteria, and user evaluations were employed to confirm the practicality and instructional utility of the final model.
The findings of the study are summarized as follows.
First, two primary criteria for classifying discussion-based instructional types were identified: the purpose of discussion and the interaction structure. The study conceptualized discussion-based instruction as comprising four instructional purposes—idea sharing, knowledge acquisition, knowledge exploration, and decision-making—combined with two interaction structures: fixed-group and cross-group (rotational). These criteria provide theoretically grounded and pedagogically actionable guidance for designing discussion lessons aligned with learners’ cognitive engagement and interactional patterns.
Second, based on these criteria, an eight-type classification system for discussion-based instruction was developed. The system includes: fixed-group idea-sharing discussion, cross-group idea-sharing discussion; fixed-group knowledge-acquisition discussion, cross-group knowledge-acquisition discussion; fixed-group knowledge-exploration discussion, cross-group knowledge-exploration discussion; fixed-group decision-making discussion, and cross-group decision-making discussion. Each type is characterized not by procedural stages but by pedagogical intention and interactional configuration, enabling instructors to flexibly select or combine discussion formats according to lesson goals, student characteristics, and instructional contexts.
Third, user evaluations conducted with practicing educators confirmed the practical validity and pedagogical usefulness of the developed system. Participants indicated that the framework effectively differentiates discussion purposes and interaction structures, thereby providing clear direction for lesson design and enhancing coherence between learning objectives and instructional activities. The clarity of each type’s characteristics enables instructors to select discussion formats that best fit the cognitive demands and situational needs of each lesson. These findings demonstrate that the classification system serves as a functional and practice-oriented tool for planning and implementing discussion-based instruction.
This study makes both theoretical and practical contributions. Theoretically, it offers a systematic and differentiated framework that integrates instructional purpose and interaction structure as core dimensions of discussion-based learning. Practically, it provides a structured guideline that assists educators in selecting suitable discussion types based on class size, learner profiles, instructional time, and the degree of teacher facilitation required. Additionally, representative techniques and application examples for each type are included in the appendix to support real-world instructional use.
Despite these contributions, the study has limitations in that the classification system has not yet undergone empirical validation through classroom implementation. Future research should apply the proposed system in actual subject-area lessons to examine its effects on learning outcomes and to conduct case studies reflecting diverse educational levels and disciplinary contexts. Further research may also explore the development of AI-supported, customized instructional design tools—such as generative AI–based systems that recommend discussion types according to teacher-input conditions—to expand the applicability of the model.
Keywords: discussion-based instruction, classification framework, discussion types, instructional design