Development of an Unsupervised LearningBased Automated Evaluation System of Descriptive Assessment
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초록
Current research on automatic scoring using traditional supervised learning methodscannot grade responses to questions that are newly generated or created spontaneously. Additionally, relying on pre-developed questions and their corresponding scoring modelsfor lesson planning may limit the creativity and diversity of instruction. This study proposesa method that can quickly evaluate student responses and generate feedback withoutthe need for pre-developed models. We introduces the SAAI system, which employsunsupervised learning techniques to instantly create scoring models based on studentresponses, thereby generating evaluation and feedback information. The SAAI systemcomplements the automatic scoring of traditional supervised learning methods andsupports scoring for a wide range of newly generated questions. This research elucidates theprinciples and significance of this system.
Abstract
Current research on automatic scoring using traditional supervised learning methodscannot grade responses to questions that are newly generated or created spontaneously. Additionally, relying on pre-developed questions and their corresponding scoring modelsfor lesson planning may limit the creativity and diversity of instruction. This study proposesa method that can quickly evaluate student responses and generate feedback withoutthe need for pre-developed models. We introduces the SAAI system, which employsunsupervised learning techniques to instantly create scoring models based on studentresponses, thereby generating evaluation and feedback information. The SAAI systemcomplements the automatic scoring of traditional supervised learning methods andsupports scoring for a wide range of newly generated questions. This research elucidates theprinciples and significance of this system.
참고문헌 (17)
- Bada, S. O., "Constructivism learning theory: A paradigm for teaching and learning", Journal of Research & Method in Education, 5, pp.66~70 (2015)
- Campbell, R. J., "Personalised learning : Ambiguities in theory and practice", British journal of educational studies, 55, pp.135~154 (2007)
- Campos, J. A., "Training language models with language feedback", ACL Workshop on Learning with Natural Language Supervision (2022)
- Dai, W., "Can large language models provide feedback to students? A case study on ChatGPT", 2023 IEEE International Conference on Advanced Learning Technologies, pp.323~325 (2023), IEEE
- Fosnot, C. T., "Constructivism: A psychological theory of learning", Constructivism:Theory, perspectives, and practice, 2, pp.8~33 (1996)
- Ha, M, "Assessing Scientific Practices Using Machine Learning Methods: Development of Automated Computer Scoring Models for Written Evolutionary Explanations", pp.2013
- Ha, M., "Applying computerized-scoring models of written biological explanations across courses and colleges : Prospects and limitations", CBE-Life Sciences Education, 10, pp.379~393 (2011)
- 한혜정, "학문중심 교육과정 및 이해중심 교육과정과의 비교를 통한 역량기반 교육과정 이해", 교육과정연구, 35(3), pp.203~221 (2017), 한국교육과정학회
- Lee, J., "Automated assessment of student hand drawings in free-response items on the particulate nature of matter", Journal of Science Education and Technology, pp.1~18 (2023)
- Moharreri, K., "EvoGrader: An online formative assessment tool for automatically evaluating written evolutionary explanations", Evolution : Education and Outreach, 7, pp.1~14 (2014)
- Nehm, R. H., "Item feature effects in evolution assessment", Journal of Research in Science Teaching, 48, pp.237~256 (2011)
- Opfer, J. E., "Cognitive foundations for science assessment design: Knowing what students know about evolution", Journal of Research in Science Teaching, 49, pp.744~777 (2012)
- Shin, D., "A systematic review on data mining for mathematics and science education", International Journal of Science and Mathematics Education, 19, pp.639~659 (2021)
- Zha, H., "Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering", Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp.113~120 (2002)
- Zhai, X., "From substitution to redefinition: A framework of machine learning‐based science assessment", Journal of Research in Science Teaching, 57, pp.1430~1459 (2020)
- Zhai, X., "Applying machine learning to automatically assess scientific models", Journal of Research in Science Teaching, 59, pp.1765~1794 (2022)
- Zhai, X., "A meta-analysis of machine learning-based science assessments:Factors impacting machine-human score agreements", Journal of Science Education and Technology, 30, pp.361~379 (2021)
