AI and Machine Learning for Next Generation Science Assessments
<|reference_start|>AI and Machine Learning for Next Generation Science Assessments: 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.<|reference_end|>
@article{zhai2024ai,
    title={AI and Machine Learning for Next Generation Science Assessments},
    author={Xiaoming Zhai},
    journal={arXiv preprint arXiv:2405.06660},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06660},
    primaryClass={physics.ed-ph cs.AI cs.CY cs.LG}
}