We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byBenjamin Johnston
Modified over 6 years ago
©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
Stages of Learning Chapter 5.
Learning and Educational Technology. Objectives To look into some principles of learning relevant to educational technology To discuss the four revolutions.
Educational data mining overview & Introduction to Exploratory Data Analysis Ken Koedinger CMU Director of PSLC Professor of Human-Computer Interaction.
Knowledge Inference: Advanced BKT Week 4 Video 5.
Improving learning by improving the cognitive model: A data- driven approach Cen, H., Koedinger, K., Junker, B. Learning Factors Analysis - A General Method.
Debating the Issue of Tutoring Interactivity: Intuition vs. Experimentation Tanner Jackson It’s a MAD MAD MAD MAD Morning.
Supporting (aspects of) self- directed learning with Cognitive Tutors Ken Koedinger CMU Director of Pittsburgh Science of Learning Center Human-Computer.
Continuous Improvement in Teaching and Learning Candace Thille Director, Open Learning Initiative.
Mohammed Abouzour, Kenneth Salem, Peter Bumbulis Presentation by Mohammed Abouzour SMDB2010.
Effective Skill Assessment Using Expectation Maximization in a Multi Network Temporal Bayesian Network By Zach Pardos, Advisors: Neil Heffernan, Carolina.
Cognitive Tutors ITS, Sept 30, Overview Production system models –For LISP, geometry, and algebra 8 principles from ACT theory Gains: 1/3 time to.
King Saud University College of nursing Master program.
Conclusion Our prediction model did a good job at predict 8 th grade math proficiency. It can be used to estimate 10 th grade score fairly well, too. But.
Circle Empirical Methods for Dialogs, June Some Goals for Evaluating Dialogue Systems Kenneth R. Koedinger Human-Computer Interaction Carnegie Mellon.
Using Mixed-Effects Modeling to Compare Different Grain-Sized Skill Models Mingyu Feng, Worcester Polytechnic Institute Neil T. Heffernan, Worcester Polytechnic.
+ Doing More with Less : Student Modeling and Performance Prediction with Reduced Content Models Yun Huang, University of Pittsburgh Yanbo Xu, Carnegie.
CLT Conference Heerlen Ron Salden, Ken Koedinger, Vincent Aleven, & Bruce McLaren (Carnegie Mellon University, Pittsburgh, USA) Does Cognitive Load Theory.
Educational data mining overview & Introduction to Exploratory Data Analysis with DataShop Ken Koedinger CMU Director of PSLC Professor of Human-Computer.
Project Management Tools and Techniques ISMC, October 23, 2001.
Learning Sciences and Engineering Professional Master’s Program Ken Koedinger Vincent Aleven Albert Corbett Carolyn Rosé Justine Cassell.
© 2021 SlidePlayer.com Inc. All rights reserved.