Date
Publisher
arXiv
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.
Study at a glance
- Question: How can artificial intelligence and machine learning support new ways of assessing complex K-12 science learning?
- Setting and data: Not stated in the abstract
- Method: Literature review and introduction of a scoring accuracy framework
Why it matters
Traditional multiple-choice assessments often fail to capture the complexities of three-dimensional scientific thinking. As education shifts toward knowledge-in-use, new performance-based assessments are needed that require students to engage in practices like modeling, explanation, and argumentation.
Key findings
- In K-12 science education, machine learning scoring systems can provide timely, objective feedback, alleviating grading burdens on teachers.
- Advanced artificial intelligence language models have shown promise in effectively assessing students' written responses.
- Traditional multiple-choice questions often fail to capture complex scientific thinking and practices like modeling, explanation, and argumentation.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
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Study design
