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Materials Similar to Multilingual Performance of a Multimodal Artificial Intelligence System on Multisubject Physics Concept Inventories

• 41%: Performance on In-Class vs. Online Administration of Concept Inventories
• 28%: Why normalized gain should continue to be used in analyzing preinstruction and postinstruction scores on concept inventories
• 28%: Performance of freely available vision-capable chatbots on the test for understanding graphs in kinematics
• 27%: Do Concept Inventories Actually Measure Anything?
• 26%: Examining evolving performance on the Force Concept Inventory using factor analysis
• 24%: In-class vs. Online Administration of Concept Inventories and Attitudinal Assessments
• 24%: Linking and comparing short and full-length concept inventories of electricity and magnetism using item response theory
• 24%: Preliminary results on a video-based force concept inventory
• 24%: Deeper look at question categories, concepts, and context covered: Modified module analysis of quantum mechanics concept assessment
• 23%: The effect of distracters on student performance on the Force Concept Inventory
• 23%: Student performance on conceptual questions: Does instruction matter?
• 23%: The Wave Concept Inventory - A Cognitive Instrument Based on Bloom’s Taxonomy
• 23%: Examining the relationship between student performance and video interactions
• 23%: Using natural language processing to predict student problem solving performance
• 22%: Learning Motion Concepts -- Sample Results from a National Collaboration of High Schools
• 22%: Learning Motion Concepts -- Sample Results from Massachusetts High Schools
• 22%: Plasticity of intermediate mechanics students’ coordinate system choice
• 22%: Gender gap on concept inventories in physics: What is consistent, what is inconsistent, and what factors influence the gap?
• 21%: Best Practices for Administering Concept Inventories