Science Teachers' Perception of Automated Scoring Scientific Argumentation in a Classroom

• 발행기관: 한국교원대학교 뇌기반교육연구소
• 저자명: Suna Ryu
• 간행물 정보: 『Brain, Digital, & Learning』제14권 제4호, 559~575쪽, 전체 17쪽
• 주제분류: 사회과학 > 교육학
• 파일형태: PDF
• 발행일자: 2024.12.31

국문 초록

This study examines science teachers' perceptions of automated scoring systems (AS) in teaching scientific reasoning in the classroom. Using a combination of diffusion theory and digital assessment literacy as an analytical framework, focus group interviews were conducted to explore teachers' attitudes, understandings, and challenges with AS. Findings suggest that teachers primarily used AS as a supplemental tool rather than a primary assessment method, relying on its insights as an additional reference throughout the course. Teachers found that understanding the concepts underlying machine learning algorithms not only increased their confidence in AS, but also inspired new approaches to integrating AI with science content. A key contribution of this study is its detailed examination of teachers' first-hand experiences with AI-enhanced learning systems, which provides insights into how AS can be effectively incorporated into science education. The findings contribute to the design of supportive AI-enhanced assessment environments that are aligned with teachers' instructional goals and pedagogical values.

목차

Introduction

Methods

Data Collection and Analysis

Results

Discussion

Conclusion

References

키워드

Teacher perception automated scoring diffusion theory digital data literacy machine learning