[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120224-en":3,"doc-seo-120224-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120224,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",4,"Exam","AI and Machine Learning for Next Generation Science Assessments - Chapter 10 - transformative role and performance-based scoring","This chapter examines how artificial intelligence (AI) and machine learning (ML) can transform science assessments under the Framework for K-12 Science Education, which emphasizes knowledge-in-use across science and engineering practices, disciplinary core ideas, and crosscutting concepts. It critiques traditional multiple-choice formats for failing to represent complex scientific thinking. The chapter argues for performance-based assessments and proposes ML-based automatic scoring approaches to deliver timely, objective feedback. It reviews ML scoring systems and discusses future challenges, including pre-trained models for evaluating written responses.","arXiv :2405 .06660v1 [physics .ed-ph] 23 Apr 2024  \nChapter  \n10  \nAI and Machine Learning for Next Generation Science Assessments  \nXiaoming Zhai 1.2  \n1AI4STEM Education Center, University of Georgia  \n2Department of Mathematics, Science, and Social Studies Education, University of Georgia  \nAbstract  \nThis chapter focuses on the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in science assessments. The paper begins with a discussion of the Framework for K- 12 Science Education, which calls for a shift from conceptual learning to knowledge-in-use. This shift necessitates the development of new types of assessments that align with the Framework’s three dimensions: science and engineering practices, disciplinary core ideas, and crosscutting concepts. The paper further highlights the limitations of traditional assessment methods like multiple-choice questions, which often fail to capture the complexities of scientific thinking and three-dimensional learning in science. It emphasizes the need for performance-based assessments that require students to engage in scientific practices like modeling, explanation, and argumentation. The paper achieves three major goals: reviewing the current state of ML-based assessments in science education, introducing a framework for scoring accuracy in ML-based automatic assessments, and discussing future directions and challenges. It delves into the evolution of ML-based automatic scoring systems, discussing various types of ML like supervised, unsupervised, and semi-supervised learning. These systems can provide timely and objec  \ntive feedback, thus alleviating the burden on teachers. The paper concludes by exploring pre-trained models like BERT and finetuned ChatGPT, which have shown promise in as sessing students’ written responses effectively.  \nCite This Chapter  \nZhai, X. (2024). AI and Machine Learning for Next Generation Science Assessments. Jiao, H., & Lissitz, R. W. (Eds.). Machine learning, natural language processing and psychometrics. Charlotte, NC: Information Age Publisher.  \n1 Introduction  \nThe rapid advancement of artificial intelligence (AI) in recent years has brought about transformative changes in various domains, including science assessment. Conventional methods of assessment in science education, particularly in classroom settings, often rely on multiple-choice questions (Zhai & Li, 2021), which may not fully capture students’ understanding and engagement with scientific practices. This problem is even pronounced with the realization of three-dimensional learning in science—integrating science and engineering practices, disciplinary core ideas, and crosscutting concepts—a new vision set forth in the Framework for K-12 Science Education (National Research Council, 2012) . This vision presents challenges for Next Generation Science Assessments because traditional assessments often fall short of capturing the complexities of scientific thinking during science and engineering practices. However, with the advent of machine learning (ML) techniques—an advanced artificial intelligence (AI), there is a growing opportunity to revolutionize the assessment practices in the science learning (Zhai, Haudek, Shi, et al., 2020) .  \nTo assess Next Generation Science Learning and foster critical thinking and problemsolving skills, efforts have been put into developing ML-and performance-based assessments, which can engage students in science and engineering practices. Performancebased assessments usually require students to observe phenomena and develop explanations, arguments, or solutions (Harris et al., 2019) . These assessment tasks require students to represent their thinking using multimodalities, such as writing or drawing (Zhai & Nehm, 2023) . However, writing and drawing are challenging to score in a timely fashion. For classroom assessment practices, without timely feedback, the promise of such assessments might be significantly compromised. That i","cbCaidixpdjWsZNy","https://ap.wps.com/l/cbCaidixpdjWsZNy","pdf",883394,1,18,"English","en",105,"# Introduction\n## Limitations of traditional multiple-choice assessments\n## Need for performance-based assessment tasks\n## ML-based automatic scoring for timely feedback\n## Goals and proposed framework","[{\"question\":\"Why do traditional science assessments struggle with next generation science learning?\",\"answer\":\"Traditional formats, especially multiple-choice questions, often fail to capture the complexities of scientific thinking and three-dimensional learning reflected in science and engineering practices, disciplinary core ideas, and crosscutting concepts.\"},{\"question\":\"What is the promise of ML-based assessments in this chapter?\",\"answer\":\"ML can automatically score written responses and drawn models, enabling timely and objective feedback that supports deeper learning and reduces teachers’ scoring burden.\"},{\"question\":\"What models and ML approaches does the chapter discuss?\",\"answer\":\"The chapter reviews ML-based automatic scoring systems, including supervised, unsupervised, and semi-supervised learning, and highlights pre-trained models such as BERT and fine-tuned ChatGPT for assessing students’ written responses.\"}]","AI and Machine Learning for Next Generation Science Assessments - 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