Real-Time AI-Driven Assessment & Scaffolding That Improves Students' Mathematical Modeling during Science Inquiry

Autor/inn/en	Adair, Amy; Segan, Ellie; Gobert, Janice; Sao Pedro, Michael
Titel	Real-Time AI-Driven Assessment & Scaffolding That Improves Students' Mathematical Modeling during Science Inquiry
Quelle	(2023), (13 Seiten)Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; Monographie
Schlagwörter	Artificial Intelligence; Mathematical Models; Science Process Skills; Inquiry; Scaffolding (Teaching Technique); Science Instruction; Intelligent Tutoring Systems; Algorithms; Student Evaluation; Computer Simulation; Middle School Students; Grade 8; Graphs; Effect Size; Instructional Effectiveness + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Mathematical model; Mathematisches Modell; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Intelligentes Tutorsystem; Algorithm; Algorithmus; Schulnote; Studentische Bewertung; Computergrafik; Computersimulation; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; School year 08; 8. Schuljahr; Schuljahr 08; Grafische Darstellung; Unterrichtserfolg
Abstract	Developing models and using mathematics are two key practices in internationally recognized science education standards, such as the Next Generation Science Standards (NGSS). However, students often struggle with these two intersecting practices, particularly when developing mathematical models about scientific phenomena. Formative performance-based assessments designed to elicit fine-grained data about students' competencies on these practices can be leveraged to develop embedded AI scaffolds to support students' learning. In this paper, we present the design and initial classroom test of virtual labs that automatically assess fine-grained sub-components of students' mathematical modeling competencies based on their actions within the learning environment. We describe how we leveraged underlying machine-learned and knowledge-engineered algorithms to trigger scaffolds, delivered proactively by a pedagogical agent, that address students' individual difficulties as they work. Results show that the students who received automated scaffolds for a given practice on their first virtual lab improved on that practice for the next virtual lab on the same science topic in a different scenario (a near-transfer task). These findings suggest that real-time automated scaffolds based on fine-grained assessment can foster students' mathematical modeling competencies related to the NGSS. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01