과학 교과 온톨로지 개발과정에서 내용 전문가들이겪는 어려움과 해결방안

Abstract

This study examines the difficulties faced by content experts in the ontology developmentprocess for science education and explores issues encountered in collaboration withdevelopers. The findings aim to provide foundational data for AI-based learning platformdevelopment. Using qualitative analysis (Lecompte & Preissle, 1993), the study investigatesthe experiences of ten content experts involved in constructing a middle school scienceontology. The difficulties encountered by content experts were categorized into three areas: entitysetting, relation setting, and attribute setting. In entity setting, difficulties arose due toambiguous curriculum descriptions, differences in curriculum interpretation among experts,selection of entities related to scientific inquiry, and lack of interdisciplinary understanding. Relation setting required discussions on entity dependency, parallelism, and alignment withthe learning sequence. In attribute setting, issues included polysemous terminology anddefining the depth and scope of learning. Similarly, collaboration with developers presenteddifficulties in these areas. Entity setting issues included curriculum structure by grade level,duplicate terminology, and errors from special characters. Relationship setting revealedinconsistencies between the curriculum’s formal hierarchy and actual learning sequences,along with differences in conceptual understanding. Attribute setting faced visualizationconstraints due to attribute length limitations. These findings provide insights for the systematic development of AI-driven personalizedlearning platforms, emphasizing the need for enhanced interdisciplinary collaboration.