Principal Investigators: James Lester (PI, Computer Science), Michael Carter (co-PI, English), Bradford Mott (co-PI, Computer Science), Eric Wiebe (co-PI, Mathematics, Science, & Technology Education)

Primary Participants: Alok Baikadi (Computer Science), John Bedward (Education), Courtney Behrle (Education), Elysa Corin (Education), Kirby Culbertson (Art & Design), Julius Goth (Computer Science), Sarah Hegler (Art & Design), Chris Kelley (Psychology), Jennifer London (Psychology), Seung Lee (Computer Science), Samuel Leeman-Munk (Computer Science), Eleni Lobene (Psychology), Chris Mitchell (Computer Science), Adam Osgood (Art & Design), Lindsay Patterson (Education), Justin Phillips (Art & Design), Cathy Ringstaff (Evaluation), Jonathan Rowe (Computer Science), Marc Russo (Art & Design), Wayne Sheffield (Education), Andy Smith (Computer Science), Robert Taylor (Computer Science), Michael Timms (Evaluation)

Sponsor: National Science Foundation – Discovery Research K-12 Program (2010-2014)

Objectives: Central to elementary science education is the development of conceptual understanding through the modeling of scientific phenomena. The process of inquiry, focused on observed and described phenomena, requires students be equipped with epistemic tools that allow them to construct extended discursive threads—synthesizing concrete observations and abstract concepts through multiple modes of representation. Students need scaffolded support in this process from sophisticated and powerful modeling tools applied along a learning progression of scientific understanding. The objective of the Leonardo project is to develop and evaluate an intelligent cyberlearning system for interactive scientific modeling in elementary science education. Students in Grades 4 and 5 will use Leonardo’s intelligent virtual science notebooks to create and experiment with interactive models of physical phenomena. The project has three major thrusts:

1. Develop CyberPads, intelligent virtual science notebooks with sketch-based multimodal interfaces. To promote effective science learning, we will design and develop CyberPads, intelligent virtual science notebooks. Leonardo’s CyberPads will be artificial intelligence-based software systems that enable students to create graphical representations to model physical phenomena. Students’ models will “come alive” as interactive media artifacts that combine animation, sound, and narration. With a curricular focus on the physical and earth sciences, CyberPads will support multimodal interactive scientific modeling for four curricular units: forces and motion, magnetism and electricity, landforms, and weather and climate.

2. Develop PadMates, intelligent virtual tutors to support interactive scientific modeling. Intelligent virtual tutors are “embodied” artificial intelligence-driven characters that interact with students to provide engaging, personalized tutorial support. We will develop Leonardo’s PadMates, who will provide customized advice and explanations during students’ interactive scientific modeling experiences with their CyberPads. Students will engage in rich interactions with their PadMates as they construct a deep understanding of science concepts and how to think scientifically.

3. Evaluate the synergistic impact of intelligent virtual science notebooks and intelligent virtual tutors on elementary science learning. To evaluate the impact of the Leonardo system on science learning in Grades 4 and 5, the evaluation will investigate the central issues of interactive scientific modeling (strategy use, divergent thinking, and collaboration) and engagement (motivation, situational interest) with respect to achievement as measured by both science content knowledge and transfer, and metacognitive aspects of scientific ways of thinking. Emphasizing connections to the National Science Education Standards and AAAS’s Benchmarks and building on our experience with school-based research, all studies will be conducted onsite at the project’s partner elementary schools in North Carolina, Texas, and California. With diverse student populations, the studies will determine precisely which technologies and conditions contribute most effectively to learning processes and outcomes.

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