مؤتمر
Real-Time AI-Driven Assessment & Scaffolding That Improves Students' Mathematical Modeling during Science Inquiry
| العنوان: | Real-Time AI-Driven Assessment & Scaffolding That Improves Students' Mathematical Modeling during Science Inquiry |
|---|---|
| اللغة: | English |
| المؤلفون: | Adair, Amy, Segan, Ellie, Gobert, Janice, Sao Pedro, Michael |
| المصدر: | Grantee Submission. 2023. |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| تاريخ النشر: | 2023 |
| Sponsoring Agency: | Institute of Education Sciences (ED) |
| Contract Number: | R305A210432 |
| نوع الوثيقة: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Junior High Schools Middle Schools Secondary Education Elementary Education Grade 8 |
| Descriptors: | 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 |
| مستخلص: | 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. |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2023 |
| رقم الأكسشن: | ED628995 |
| قاعدة البيانات: | ERIC |
| الوصف غير متاح. |