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Direct linkLee, Hee-Sun; Gweon, Gey-Hong; Lord, Trudi; Paessel, Noah; Pallant, Amy; Pryputniewicz, Sarah – Journal of Science Education and Technology, 2021
A design study was conducted to test a machine learning (ML)-enabled automated feedback system developed to support students' revision of scientific arguments using data from published sources and simulations. This paper focuses on three simulation-based scientific argumentation tasks called Trap, Aquifer, and Supply. These tasks were part of an…
Descriptors: Artificial Intelligence, Automation, Feedback (Response), Persuasive Discourse
