인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study proposes a staged and modular model for undergraduate digital history education and examines how practice modules are selected, modified, and combined in student-led research. It analyzes three courses taught by the same instructor between 2025 and 2026 using instructional materials, code, datasets, presentations, academic posters, and feedback. Fourteen student projects are examined for patterns of methodological selection and combination, while five history-based projects are analyzed in terms of data construction, analytical design, and methodological suitability.
The model consists of three cumulative stages. The first, the datafication of historical sources, teaches students to define units of analysis, attributes, identifiers, and measurements while recognizing data construction as a process of interpretation and source criticism. The second introduces statistics, historical GIS, text mining, and network analysis through model exercises and their application to new materials. The third uses generative AI to implement researcher-defined data structures and analytical conditions, while retrieval-augmented generation connects data construction, retrieval, generation, and source-based verification.
Project-based learning extends these guided exercises into open-ended historical inquiry. Rather than applying every method uniformly, students selected, modified, and combined modules according to their questions and sources. They redefined analytical units, assigned different roles to multiple methods, and limited techniques that did not fully suit their research purposes. Because each stage can be taught independently and recombined, the model may be distributed across instructors, courses, or semesters. Digital history education should therefore cultivate the ability to evaluate how historical sources are structured, analyzed, and interpreted, rather than simply teach proficiency in particular tools.
The model consists of three cumulative stages. The first, the datafication of historical sources, teaches students to define units of analysis, attributes, identifiers, and measurements while recognizing data construction as a process of interpretation and source criticism. The second introduces statistics, historical GIS, text mining, and network analysis through model exercises and their application to new materials. The third uses generative AI to implement researcher-defined data structures and analytical conditions, while retrieval-augmented generation connects data construction, retrieval, generation, and source-based verification.
Project-based learning extends these guided exercises into open-ended historical inquiry. Rather than applying every method uniformly, students selected, modified, and combined modules according to their questions and sources. They redefined analytical units, assigned different roles to multiple methods, and limited techniques that did not fully suit their research purposes. Because each stage can be taught independently and recombined, the model may be distributed across instructors, courses, or semesters. Digital history education should therefore cultivate the ability to evaluate how historical sources are structured, analyzed, and interpreted, rather than simply teach proficiency in particular tools.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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