An Analysis of the Evaluation Structure and Criteria of Self-evaluation and Peer evaluation in Project-Based Learning (PBL) within University Teacher Education Courses
본 연구는 간호학과 교직과목에서 시행된 문제중심학습(PBL) 활동을 대상으로 자기평가·동료평 가·교수평가 간 관계를 분석하고, 학습자가 실제 평가 과정에서 활용한 평가 준거를 탐색하였다. 양적 분석 을 위해 평가 점수(자기·동료·교수평가) 간 평균 및 상관관계를 비교하고, 질적 분석을 위해 성찰일지 및 동 료평가 서술형 문항을 내용분석하여 핵심 준거를 범주화하였다. 질적 분석은 반복적으로 등장하는 의미 단 위를 추출한 뒤, 유사 의미를 통합하여 상위 범주로 정련하는 절차로 수행하였다. 연구 결과, 자기평가는 동료평가에 비해 관대하게 나타났으며, 동료평가 점수는 교수평가와 상대적으로 더 근접한 양상을 보였다. 또한 학생들의 실제 평가 준거는 책임감, 역할 수행의 성실성, 적극적 참여, 피드백 제공, 협동학습능력 등 이 중심을 이루었다. PBL 평가 신뢰도 제고를 위해 평가 사전교육, 상호작용 중심 수업 구조 설계, 형성평 가적 피드백 제공의 필요성이 제안되었다. 아울러 최근 연구동향을 반영하여 AI 기반 성찰 분석, 학습로그 분석(learning analytics) 등 기술 기반 평가의 가능성을 논의하였다.
This study analyzed the relationships among self-evaluation, peer- evaluation, and instructor-evaluation in project-based learning(PBL) activities implemented in teacher-education courses within a nursing program, and explored the evaluation criteria actually used by learners during the evaluation process. For the quantitative analysis, mean differences and correlations among evaluation scores(self, peer, and instructor evaluations) were examined. For the qualitative analysis, reflection journals and open-ended peer-evaluation responses were subjected to content analysis to categorize core evaluative criteria. The qualitative procedure involved extracting repeatedly occurring meaning units and integrating similar meanings into higher-order categories. The results showed that self-evaluation tended to be more lenient than peer evaluation, while peer-evaluation scores were relatively closer to instructor-evaluation scores. In addition, the evaluative criteria actually employed by students primarily focused on responsibility, sincerity in role performance, active participation, provision of feedback, and collaborative learning competence. To enhance the reliability of PBL evaluation, the study suggests the need for pre-evaluation training, the design of interaction-centered instructional structures, and the provision of formative feedback. Furthermore, reflecting recent research trends, the study discusses the potential of technology-based evaluation approaches, such as AI-supported reflection analysis and learning analytics based on learning logs.