Studying the effect of tutor learning using a teachable agent that asks the student tutor for explanations

Proceedings 2012 4th IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning, DIGITEL 2012

Volumn , Issue , 2012, Pages 25-32

  Matsuda, Noboru ^a   Cohen, William W ^a   Koedinger, Kenneth R ^a   Keiser, Victoria ^a   Raizada, Rohan ^a   Yarzebinski, Evelyn ^a   Watson, Shayna P ^a   Stylianides, Gabriel ^b  

^a Carnegie Mellon University   (United States)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

learning by teaching; machine learning; Sim Student; teachable agent; tutor learning effect

Indexed keywords

CLASSROOM STUDY; COMPUTATIONAL MODEL; LEARNING BY TEACHING; LEARNING EFFECTS; LEARNING GAIN; SELF EXPLANATIONS; TEACHABLE AGENTS;

COMPUTER SOFTWARE; STUDENTS; TEACHING;

LEARNING SYSTEMS;

EID: 84860821398     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DIGITEL.2012.12     Document Type: Conference Paper

References (25)

1. Roscoe, R.D. and M.T.H. Chi, Understanding tutor learning: Knowledge-building and knowledge-telling in peer tutors' explanations and questions. Review of Educational Research, 2007. 77(4): p. 534-574.
2. Biswas, G., et al., Learning by teaching: a new agent paradigm for educational software. Journal Applied Artificial Intelligence, 2005. 19(3&4): p. 363-392. (Pubitemid 40500036)
3. Bredeweg, B., et al., DynaLearn - Engaging and Informed Tools for Learning Conceptual System Knowledge, in Cognitive and Metacognitive Educational Systems (MCES2009), AAAI Fall Symposium, P. R., R. Azevedo, and G. Biswas, Editors. 2009, AAAI Press: Arlington, VA. p. 46-51.
4. Nichols, D.M., Intelligent Student Systems: an Application of Viewpoints to Intelligent Learning Environments, 1993, Lancaster University: Lancaster, UK.
5. Palthepu, S., J. Greer, and G. McCalla, Learning by teaching, in Proceedings of the International Conference on Learning Sciences1991: Illinois.
6. Michie, D., A. Paterson, and J.E. Hayes, Learning by teaching, in Proc. of Second Scandinabian Conference on Artificial Intelligence1989: Tampere, Finland. p. 413-436.
7. Jun, Y., Linear Kid: A mathematical software designed as a computer-based peer tutoring system, in Proceedings of the Second Asian Technology Conference in Mathematics1997.
8. Brophy, S., et al., Teachable agents: Combining insights from learning theory and computer science, in Proceedings of the International Conference on Artificial intelligence in education, S.P. Lajoie and M. Vivet, Editors. 1999, IOS Press: Amsterdam, The Netherlands. p. 21-28.
9. Matsuda, N., et al., Applying Machine Learning to Cognitive Modeling for Cognitive Tutors, in Machine Learning Department Technical Report (CMU-ML-06-105)2006, School of Computer Science, Carnegie Mellon University: Pittsburgh, PA.
10. Matsuda, N., et al., A Computational Model of How Learner Errors Arise from Weak Prior Knowledge, in Proceedings of the Annual Conference of the Cognitive Science Society, N. Taatgen and H. van Rijn, Editors. 2009, Cognitive Science Society: Austin, TX. p. 1288-1293.
11. Matsuda, N., et al., Learning by Teaching SimStudent - An Initial Classroom Baseline Study comparing with Cognitive Tutor, in Proceedings of the International Conference on Artificial Intelligence in Education, G. Biswas and S. Bull, Editors. 2011, Springer. p. 213-221.
12. Chi, M.T., Constructing self-explanations and scaffolded explanations in tutoring. Applied Cognitive Psychology, 1996. 10(Spec Issue): p. S33-S49. (Pubitemid 126470730)
13. Grosse, C.S. and A. Renkl, Finding and fixing errors in worked examples: Can this foster learning outcomes? Learning and Instruction, 2007. 17(6): p. 612-634.
14. Ur, S. and K. VanLehn, STEPS: A Simulated, Tutorable Physics Student. Journal of Articial Intelligence in Education, 1995. 6(4): p. 405-437.
15. Chan, T.-W. and C.-Y. Chou, Exploring the Design of Computer Supports for Reciprocal Tutoring. International Journal of Artificial Intelligence in Education, 1997. 8: p. 1-29.
16. Pareto, L., et al., A Teachable-Agent Arithmetic Game's Effects on Mathematics Understanding, Attitude and Self-efficacy, in Proceedngs of the International conference on Artificial Intelligence in Education, G. Biswas, et al., Editors. 2011, Springer: Heidelberg, Berlin. p. 247-255.
17. Biswas, G., et al., Measuring Self-Regulated Learning Skills through Social Interactions in a teachable Agent Environment. Research and Practice in Technology Enhanced Learning, 2010: p. 123-152.
18. Chin, D., et al., Preparing students for future learning with Teachable Agents. Educational Technology Research and Development, 2010. 58(6): p. 649-669.
19. Lau, T.A. and D.S. Weld, Programming by demonstration: an inductive learning formulation in Proceedings of the 4th international conference on Intelligent user interfaces 1998 ACM Press New York, NY. p. 145-152.
20. VanLehn, K., Bugs are not enough: Empirical studies of bugs, impasses and repairs in procedural skills. Journal of Mathematical Behavior, 1982. 3(2): p. 3-71.
21. Chi, M.T., et al., Eliciting self-explanations improves understanding. Cognitive Science, 1994. 18(3): p. 439-477.
22. Muggleton, S. and L. de Raedt, Inductive Logic Programming: Theory and methods. Journal of Logic Programming, 1994. 19-20(Supplement 1): p. 629-679.
23. Mitchell, T.M., Machine Learning1997, Boston, MA: McGraw-Hill.
24. Russell, S.J. and P. Norvig, Artificial Intelligence: A Modern Approach. 2nd ed2003, Upper Saddle River, NJ: Prentice Hall.
25. Ritter, S., et al., Cognitive tutor: Applied research in mathematics education. Psychonomic Bulletin & Review, 2007. 14(2): p. 249-255.