Adaptive Support for Connection Making among Multiple Representations
To succeed in STEM, students need two types of representational competencies; the ability to conceptually make sense of visual representations and perceptual fluency (similar to fluency in a language). We know little about how conceptual and perceptual competencies interact. Studies in chemistry courses at UW-Madison and Madison College address this question. Based on the results, we develop an educational technology that adapts to the individual's learning of these competencies: Chem Tutor. Funded by NSF DUE 1611782 (IUSE).
Modeling Perceptual Fluency with Visual Representations
STEM learning builds on perceptual fluency: the ability to “see” visual information implicitly and effortlessly. Common instruction focuses on explicit learning processes but cannot assess implicit processes that yield perceptual fluency and hence cannot adapt to students' perceptual fluency. To address this issue, studies test how students and experts perceive visual representations. Based on the results, we design perceptual fluency activities that use machine learning to adapt to students' needs. Funded by NSF IIS 1623605 (Cyberlearning).
Intelligent Blending of Physical and Virtual Representations
Physical and virtual representations have complementary benefits. Effective combinations of these representation modes depend on topic-specific conceptual and embodied knowledge. Hence, effective combinations are likely too complex for instructors to achieve without support. We investigate how physical and virtual representations complement one another. Results will inform the development of a an adaptive educational technology that intelligently blends physical and virtual representations. Funded by NSF IIS 1651781 (CAREER).
Learning with visual representations is socially mediated: students often collaboratively make sense of visual representations. We investigate how collaboration can enhance the effectiveness of representation support offered by educational technologies, while also using educational technologies to support effective collaboration. Studies in chemistry courses test how to use the cognitive model of Chem Tutor to identify individual misconceptions and to prompt students to collaboratively discuss mistakes resulting from these misconceptions.
Drawing in Educational Technologies
Educational technologies typically support conceptual learning processes by asking students to verbally explain concepts. When students work with visual representations, they may engage in visual rather than verbal processes. Drawing is an effective learning strategy, but we know little about how drawing helps students' learning with conventional representations. We investigate whether asking students to draw their own representations enhances the effectiveness of an educational technology that focuses on representational competencies.
Leveraging the Rational Brain
Neuro-science studies show that we are born with perceptual knowledge about magnitudes. We investigate how we can activate this perceptual knowledge using visual representations that students cannot count. Studies with elementary-school students investigate how to help students connect perceptual knowledge to conceptual knowledge about commonly used fractions representations. Based on the results, we develop a new version of the Fractions Tutor that leverages students' implicit perceptual knowledge to help them learn fractions.