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Connecting Curriculum to Problem-Solving Through Distributed Intelligent Tutoring Sharon J. Derry Dept. of Educational Psychology Wisconsin Center for Education Research University of Wisconsin-Madison Contributors Mary Leonard Donald Wortham Alan Hackbarth Margaret Wilsman Michael Peterson In Nintendo's "The Legend of Zelda™," Link must travel through many rooms arranged in a coordinate maze to find the silver arrow. From the entry room, he goes right 3 rooms, up 4 rooms, left 5 rooms, down 2 rooms, left 1 room, up 3 rooms, and right 4 rooms to find the chamber that has the silver arrow. If the entry room has coordinates (0,0) and all directions are as you view the maze, find the coordinates of the room with the arrow. Making School Curricula Useful and Interesting to Students Curricular domains – Statistics, Algebra, Earth Systems Science Cognitive Theoretical Approach – “Authentic” problem contexts for learning – Relevant, knowledge-appropriate problems Instructional strategies allowing choices in problem framing and selection Projects TiPS Intelligent Tutor – Adult basic skills math Situated Simulations for Statistics – Middle school math, sci, social studies – Pre-service teachers “The Learning Sciences” through Instructional Design – Pre-service teachers Studies of Inquiry Science Classrooms – Middle schools Results/Findings Moderate to Good Learning Gains Desired Performance Improvements Over Comparison Classrooms Resource Intensive Mixed Reviews Conclusion: – Teaching and learning curriculum in the context of authentic problem solving requiring self-directed problem framing and learning presents tough instructional design challenges. The Trojan Horse Challenges I. Problem Finding II. Domain “Disorganization” III. The Modeling Problem Design Challenges in ITS Terms Modeling Domain Knowledge Modeling Students’ Domain Knowledge Problem Modeling With Domain Concepts Real-Time Modeling of Students’ Evolving Solutions Modeling Students’ Affective States Scaffolding Student Problem Solving Selecting Tutorial Digressions Machine Learning I. Problem Finding 1. No constraints on students’ problem choice. 2. Full constraints on students’ problem choice. 3. Guided/mentored problem framing. Full Constraints on Choice Examples: – Math problems matching goals are assigned. Advantages: – – – – Problems match student needs, curriculum. It's expected, accepted, desired. Arguably appropriate for early learning. Does not “reorganize“ domain. Challenges: – Students do not learn to find/frame problems. – Problems do not arise from interests. – Problems often contrived, trivial. Guided Problem Finding Examples – Practice-based professional development – Project-based learning Advantages – Learners find personally relevant problems Dilemmas – Finding curriculum-appropriate problem without prior knowledge of curriculum? – How model problem framing? – Important problems “disorganize” curriculum. II. The Domain “Disorganization Problem” LEARNING SCIENCES COGNITIVE THEORY PROB 1 PROB 2 PROB 3 IP VIEW SOCIO COGNI TIVE X-THEORY IDEAS SOCIO CULTURAL THEORY Challenges I. Problem Finding II. Domain “Disorganization” III. The Modeling Problem The Modeling Problem: A Story The authentic context: Building balloon Cars (LBD™) A curriculum goal: Newton’s Third Law Summary of Teacher’s Modeling Makes tutorial digression Models operation of car with abstract physics concepts – Develops intermediate representation making tradeoffs on what “not” to model Exhibits in-depth knowledge of physics Engages in critical reflective practice regarding representational system and success of her lecture ITS’s Intelligent Learning Communities New 21st Century Goals for Learners: – Participate in interdisciplinary learning communities that use subject knowledge to frame and solve real problems. – Acquire mindsets and skills for lifelong learning, including ability to use human and technology resources to acquire knowledge during problem solving. – Develop concern for real-world needs and willingness to become engaged citizens. Hands-On Environmentalism Community Standards Evolving Case Library ESS Project Case Templates (epistemological commitments) Continuing Community: sets Collaborative Tools Project Collaborations Scientists, Teachers, Facilitators, Env. Groups, K-12 Students seed ESS Web Resources Enrolled Learners Everything is vague to a degree you do not realize till you have tried to make it precise. Bertrand Russell