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©2012 Carnegie Learning, Inc. In-vivo Experimentation Steve Ritter Founder and Chief Scientist Carnegie Learning
©2012 Carnegie Learning, Inc. An attempt to find meaning in three acts Design: Geometry Contiguity (Vincent Aleven, Kirsten Butcher) Modeling: Adjusting learning curve parameters (Cen, Koedinger, Junker) Personalization: Word problem content (Candace Walkington)
©2012 Carnegie Learning, Inc. DESIGN
©2012 Carnegie Learning, Inc. Geometry angles
©2012 Carnegie Learning, Inc. Contiguity Early Version Commercial Version (Carnegie Learning) Research Version (Carnegie Mellon) Butcher, K., & Aleven, V. (2008). Diagram interaction during intelligent tutoring in geometry: Support for knowledge retention and deep transfer. In C. Schunn (Ed.) Proceedings of the Annual Meeting of the Cognitive Science Society, CogSci 2008. New York, NY: Lawrence Earlbaum. Hausmann, R.G.M. & Vuong, A. (2012) Testing the Split Attention Effect on Learning in a Natural Educational Setting Using an Intelligent Tutoring System for Geometry. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pp. 438-443). Austin, TX: Cognitive Science Society.
©2012 Carnegie Learning, Inc. Early Tutor
©2012 Carnegie Learning, Inc. Revised (commercial) tutor
©2012 Carnegie Learning, Inc. Geometry Contiguity Design and field experimentation –Butcher and Aleven (2008) Diagram interaction led to better transfer and retention Analysis of impact –Hausmann and Vuong (2012) Unit-level effects mixed Advantage for harder skills
©2012 Carnegie Learning, Inc. Geometry Angles
©2012 Carnegie Learning, Inc. Lessons Change is constant Transition from research to production always requires adaptation
©2012 Carnegie Learning, Inc. MODELING
©2012 Carnegie Learning, Inc. Skillometer
©2012 Carnegie Learning, Inc. Expression Writing
©2012 Carnegie Learning, Inc. What gets learned?
©2012 Carnegie Learning, Inc. Bayesian Knowledge Tracing Cognitive tutor traces these skills differently
©2012 Carnegie Learning, Inc. Learning Curve Parameter Fitting Field study looking at learning area of geometric figures –One group used adjusted learning parameters based on previous year’s data Optimized group took 12% less time to reach same performance Significant learning gain in both groups No difference in learning gain between groups (p = 0.772 ) 16
©2012 Carnegie Learning, Inc. Lessons Learning efficiency is a great outcome Small, systemic changes can have big impact Optimizing skills requires appropriate skill model –Koedinger, McLaughlin and Stamper (2012) - LFA
©2012 Carnegie Learning, Inc. PERSONALIZATION
©2012 Carnegie Learning, Inc. Word problem customization
©2012 Carnegie Learning, Inc. Personalization field study Students who got problems related to their interests made fewer errors Also affected subsequent unit Interaction with readability
©2012 Carnegie Learning, Inc. Lessons Content matters –Challenge for knowledge component modeling Are we personalizing preferences, reading level or both?
©2012 Carnegie Learning, Inc. Summary It’s not about whether A is better than B –It’s about why A is better than B
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