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    Development of an Unsupervised LearningBased Automated Evaluation System of Descriptive Assessment

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    https://www.riss.kr/link?id=A108899097

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    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.
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    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 ...

    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.

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    참고문헌 (Reference)

    1 한혜정 ; 이주연, "학문중심 교육과정 및 이해중심 교육과정과의 비교를 통한 역량기반 교육과정 이해" 한국교육과정학회 35 (35): 203-221, 2017

    2 Campos, J. A., "Training language models with language feedback" 2022

    3 Campbell, R. J., "Personalised learning : Ambiguities in theory and practice" 55 : 135-154, 2007

    4 Nehm, R. H., "Item feature effects in evolution assessment" 48 : 237-256, 2011

    5 Zha, H., "Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering" 113-120, 2002

    6 Zhai, X., "From substitution to redefinition: A framework of machine learning‐based science assessment" 57 : 1430-1459, 2020

    7 Moharreri, K., "EvoGrader: An online formative assessment tool for automatically evaluating written evolutionary explanations" 7 : 1-14, 2014

    8 Fosnot, C. T., "Constructivism: A psychological theory of learning" 2 : 8-33, 1996

    9 Bada, S. O., "Constructivism learning theory: A paradigm for teaching and learning" 5 : 66-70, 2015

    10 Opfer, J. E., "Cognitive foundations for science assessment design: Knowing what students know about evolution" 49 : 744-777, 2012

    1 한혜정 ; 이주연, "학문중심 교육과정 및 이해중심 교육과정과의 비교를 통한 역량기반 교육과정 이해" 한국교육과정학회 35 (35): 203-221, 2017

    2 Campos, J. A., "Training language models with language feedback" 2022

    3 Campbell, R. J., "Personalised learning : Ambiguities in theory and practice" 55 : 135-154, 2007

    4 Nehm, R. H., "Item feature effects in evolution assessment" 48 : 237-256, 2011

    5 Zha, H., "Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering" 113-120, 2002

    6 Zhai, X., "From substitution to redefinition: A framework of machine learning‐based science assessment" 57 : 1430-1459, 2020

    7 Moharreri, K., "EvoGrader: An online formative assessment tool for automatically evaluating written evolutionary explanations" 7 : 1-14, 2014

    8 Fosnot, C. T., "Constructivism: A psychological theory of learning" 2 : 8-33, 1996

    9 Bada, S. O., "Constructivism learning theory: A paradigm for teaching and learning" 5 : 66-70, 2015

    10 Opfer, J. E., "Cognitive foundations for science assessment design: Knowing what students know about evolution" 49 : 744-777, 2012

    11 Dai, W., "Can large language models provide feedback to students? A case study on ChatGPT" IEEE 323-325, 2023

    12 Lee, J., "Automated assessment of student hand drawings in free-response items on the particulate nature of matter" 1-18, 2023

    13 Ha, M, "Assessing Scientific Practices Using Machine Learning Methods: Development of Automated Computer Scoring Models for Written Evolutionary Explanations" The Ohio State University 2013

    14 Zhai, X., "Applying machine learning to automatically assess scientific models" 59 : 1765-1794, 2022

    15 Ha, M., "Applying computerized-scoring models of written biological explanations across courses and colleges : Prospects and limitations" 10 : 379-393, 2011

    16 Shin, D., "A systematic review on data mining for mathematics and science education" 19 : 639-659, 2021

    17 Zhai, X., "A meta-analysis of machine learning-based science assessments:Factors impacting machine-human score agreements" 30 : 361-379, 2021

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