AI and Machine Learning for Next Generation Science Assessments
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910443931435008 |
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| author | Zhai, Xiaoming |
| author_facet | Zhai, Xiaoming |
| contents | This 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 objective 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 assessing students' written responses effectively. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_06660 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI and Machine Learning for Next Generation Science Assessments Zhai, Xiaoming Physics Education Artificial Intelligence Computers and Society Machine Learning This 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 objective 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 assessing students' written responses effectively. |
| title | AI and Machine Learning for Next Generation Science Assessments |
| topic | Physics Education Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2405.06660 |