From Design to Implementation to Practice a Learning by Teaching System: Betty’s Brain

  • Gautam Biswas
  • James R. Segedy
  • Kritya Bunchongchit


This paper presents an overview of 10 years of research with the Betty’s Brain computer-based learning environment. We discuss the theoretical basis for Betty’s Brain and the learning-by-teaching paradigm. We also highlight our key research findings, and discuss how these findings have shaped subsequent research. Throughout the course of this research, our goal has been to help students become effective and independent science learners. In general, our results have demonstrated that the learning by teaching paradigm implemented as a computer based learning environment (specifically the Betty’s Brain system) provides a social framework that engages students and helps them learn. However, students also face difficulties when going about the complex tasks of learning, constructing, and analyzing their learned science models. We have developed approaches for identifying and supporting students who have difficulties in the environment, and we are actively working toward adding more adaptive scaffolding functionality to support student learning.


Open-ended learning environments Learning by teaching Pedagogical agents Adaptive scaffolding Coherence analysis Characterizing student behaviors 


  1. Adams, E. (2010). Fundamentals of game design (2nd ed.). Berkeley: New Riders Pub.Google Scholar
  2. Arias, E., Eden, H., Fischer, G., Gorman, A., & Scharff, E. (2000). Transcending the individual human mind—creating shared understanding through collaborative design. ACM Transactions on Computer-Human Interaction (TOCHI), 7(1), 84–113.CrossRefGoogle Scholar
  3. Bargh, J. A., & Schul, Y. (1980). On the cognitive benefits of teaching. Journal of Educational Psychology, 72(5), 593–604.CrossRefGoogle Scholar
  4. Barron, B. (2000). Achieving coordination in collaborative problem-solving groups. The Journal of the Learning Sciences, 9(4), 403–436.CrossRefGoogle Scholar
  5. Benware, C. A., & Deci, E. L. (1984). Quality of learning with an active versus passive motivational set. American Educational Research Journal, 21(4), 755–765.CrossRefGoogle Scholar
  6. Biswas, G., Schwartz, D., & Bransford, J. (2001). Technology support for complex problem solving: From SAD environments to AI. In K. D. Forbus & P. J. Feltovich (Eds.), Smart machines in education (pp. 71–98). Menlo Park: AAAI Press.Google Scholar
  7. Biswas, G., Leelawong, K., Belynne, K., & Adebiyi, B. (2005). Case studies in learning by teaching behavioral differences in directed versus guided learning. In: Proceedings of the 27th Annual Conference of the Cognitive Science Society (pp. 828–833). Stresa, Italy.Google Scholar
  8. Biswas, G., Leelawong, K., Schwartz, D., & Vye, N. (2005b). Learning by teaching: a new agent paradigm for educational software. Applied Artificial Intelligence, 19(3–4), 363–392.CrossRefGoogle Scholar
  9. Biswas, G., Jeong, H., Kinnebrew, J., Sulcer, B., & Roscoe, R. (2010). Measuring self-regulated learning skills through social interactions in a teachable agent environment. Research and Practice in Technology Enhanced Learning, 5(2), 123–152.CrossRefGoogle Scholar
  10. Bransford, J., & Schwartz, D. (1999). Rethinking transfer: a simple proposal with multiple implications. Review of Research in Education, 24(1), 61–101.CrossRefGoogle Scholar
  11. Bransford, J. D., Sherwood, R. D., Hasselbring, T. S., Kinzer, C. K., & Williams, S. M. (1990). Anchored instruction: Why we need it and how technology can help. In D. Nix & R. J. Spiro (Eds.), Cognition, education, and multimedia: exploring ideas in high technology (pp. 115–141). Hillsdale: L. Erlbaum.Google Scholar
  12. Bransford, J., Brown, A., & Cocking, R. (Eds.). (2000). How People Learn. Washington, D.C.: National Academy Press.Google Scholar
  13. Brophy, S., Biswas, G., Katzlberger, T., Bransford, J., & Schwartz, D. (1999). Teachable agents: Combining insights from learning theory and computer science. In S. P. Lajoie & M. Vivet (Eds.), Artificial intelligence in education (pp. 21–28). Amsterdam: IOS Press.Google Scholar
  14. Chan, T. W., & Chou, C. Y. (1997). Exploring the design of computer supports for reciprocal tutoring. International Journal of Artificial Intelligence in Education, 8, 1–29.Google Scholar
  15. Chase, C. C., Chin, D. B., Oppezzo, M. A., & Schwartz, D. L. (2009). Teachable agents and the protégé effect: increasing the effort towards learning. Journal of Science Education and Technology, 18(4), 334–352.CrossRefGoogle Scholar
  16. Clarebout, G., & Elen, J. (2008). Advice on tool use in open learning environments. Journal of Educational Multimedia and Hypermedia, 17(1), 81–97.Google Scholar
  17. Cobb, P., & Bowers, J. (1999). Cognitive and situated learning perspectives in theory and practice. Educational Researcher, 28(2), 4–15.CrossRefGoogle Scholar
  18. Cohen, P., Kulik, J., & Kulik, C. (1982). Educational outcomes of tutoring: a meta-analysis of findings. American Educational Research Journal, 19(2), 237–248.CrossRefGoogle Scholar
  19. Dillenbourg, P. (1999). What do you mean by collaborative learning? Collaborative-learning: Cognitive and Computational Approaches (pp. 1–19). Oxford: Elsevier.Google Scholar
  20. Goos, M. (1994). Metacognitive decision making and social interactions during paired problem solving. Mathematics Education Research Journal, 6, 144–165.CrossRefGoogle Scholar
  21. Kinnebrew, J. & Biswas, G. (2012). Identifying learning behaviors by contextualizing differential sequence mining with action features and performance evolution. In: K. Yacef, O. Zaîane, H. Hershkovitz, M. Yudelson, & J. Stamper (Eds.) Proceedings of the 5th International Conference on Educational Data Mining (pp. 57–64). International Educational Data Mining Society.Google Scholar
  22. Kinnebrew, J., Loretz, K., & Biswas, G. (2013). A contextualized, differential sequence mining method to derive students’ learning behavior patterns. Journal of Educational Data Mining, 5(1), 190–219.Google Scholar
  23. Kinnebrew, J. S., Segedy, J. R., & Biswas, G. (2014). Analyzing the temporal evolution of students’ behavior in open-ended learning environments. Metacognition and Learning, 9(2), 187–215.CrossRefGoogle Scholar
  24. Land, S. (2000). Cognitive requirements for learning with open-ended learning environments. Educational Technology Research and Development, 48(3), 61–78.CrossRefGoogle Scholar
  25. Land, S., Hannafin, M., & Oliver, K. (2012). Student-centered learning environments: Foundations, assumptions and design. In D. Jonassen & S. Land (Eds.), Theoretical foundations of learning environments (pp. 3–25). New York: Routledge.Google Scholar
  26. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  27. Leelawong, K., & Biswas, G. (2008). Designing learning by teaching agents: The Betty’s Brain system. International Journal of Artificial Intelligence in Education, 18(3), 181–208.Google Scholar
  28. Michie, D., Paterson, A., & Hayes-Michie, J. (1989). Learning by Teaching. In: 2nd Scandinavian Conference on Artificial Intelligence 89 (pp. 307–331). Amsterdam: IOS Press.Google Scholar
  29. Obayashi, F., Shimoda, H., & Yoshikawa, H. (2000). Construction and evaluation of CAI system based on learning by teaching to virtual student. World Multiconference on Systemics, Cybernetics and Informatics (pp. 94–99). Volume 3. Orlando, Florida.Google Scholar
  30. Palincsar, A. S. (1998). Social constructivist perspectives on teaching and learning. Annual Review of Psychology, 49, 345–375.CrossRefGoogle Scholar
  31. Palincsar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1, 117–175.CrossRefGoogle Scholar
  32. Palincsar, A. S., Anderson, C., & David, Y. M. (1993). Pursuing scientific literacy in the middle grades through collaborative problem solving. The Elementary School Journal, 93(5), 643–658.CrossRefGoogle Scholar
  33. Palthepu, S., Greer, J., & McCalla, G. (1991). Learning by teaching. In The Proceedings of the International Conference on the Learning Sciences (pp. 357–363). Charlottesville, VA: Association for the Advancement of Computing in Education.Google Scholar
  34. Roscoe, R., & Chi, M. (2007). Understanding tutor learning: knowledge-building and knowledge-telling in peer tutors’ explanations and questions. Review of Educational Research, 77(4), 534–574.CrossRefGoogle Scholar
  35. Scardamalia, M., & Bereiter, C. (1989). Conceptions of teaching and approaches to core problems. In M. C. Reynolds (Ed.), Knowledge base for the beginning teacher (pp. 37–46). New York: Pergamon Press.Google Scholar
  36. Schwartz, D. L., Blair, K. P., Biswas, G., Leelawong, K., & Davis, J. (2007). Animations of thought: Interactivity in the teachable agent paradigm. In R. Lowe & W. Schnotz (Eds.), Learning with animation: Research and implications for design (pp. 114–140). UK: Cambrige University Press.Google Scholar
  37. Segedy, J. R. (2014). Adaptive scaffolds in open-ended computer-based learning environments (Doctoral dissertation). Nashville: Department of EECS, Vanderbilt University.Google Scholar
  38. Segedy, J.R., Kinnebrew, J., & Biswas, G. (2012a). Relating student performance to action out-comes and context in a choice-rich learning environment. In: S. Cerri, W. Clancey, G. Papadourakis, & L. Panourgia (Eds.), Intelligent Tutoring Systems: Vol. 7315. Lecture Notes in Computer Science (pp. 505–510). Springer.Google Scholar
  39. Segedy, J.R., Kinnebrew, J., and Biswas, G. (2012b). Supporting student learning using conversational agents in a teachable agent environment. In J. van Aalst, K. Thompson, M. Jacobson, & P. Reimann (Eds.), The future of learning: Proceedings of the 10th international conference of the learning sciences (ICLS 2012): Vol. 2. Short Papers, Symposia, and Abstracts (pp. 251–255). International Society of the Learning Sciences.Google Scholar
  40. Segedy, J.R., Biswas, G., Blackstock, E., & Jenkins, A. (2013a). Guided skill practice as an adaptive scaffolding strategy in open-ended learning environments. In: H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik, (Eds.), Artificial Intelligence in Education: Vol. 7926. Lecture Notes in Computer Science (pp. 532–541). Springer.Google Scholar
  41. Segedy, J. R., Kinnebrew, J., & Biswas, G. (2013b). The effect of contextualized conversational feedback in a complex open-ended learning environment. Educational Technology Research and Development, 61(1), 71–89.CrossRefGoogle Scholar
  42. Segedy, J. R., Biswas, G., & Sulcer, B. (2014). A model-based behavior analysis approach for open-ended environments. Journal of Educational Technology & Society, 17(1), 272–282.Google Scholar
  43. Segedy, J.R., Kinnebrew, J.S., & Biswas, G. (2015). Using coherence analysis to characterize self-regulated learning behaviours in open-ended learning environments. Journal of Learning Analytics, 2(1), in press.Google Scholar
  44. Tan, J., & Biswas, G. (2006). The role of feedback in preparation for future learning: A case study in learning by teaching environments. In Intelligent Tutoring Systems: Vol. 4053. Lecture Notes in Computer Science (pp. 370–381). Springer.Google Scholar
  45. Tan, J., Biswas, G., & Schwartz, D. (2006). Feedback for metacognitive support in learning by teaching environments. In Proceedings of the 28th Annual Meeting of the Cognitive Science Society (pp. 828–833). Vancouver, Canada.Google Scholar
  46. Vygotsky, L. (1978). Mind in society: The development of higher psychological processes. Cambridge: Harvard University Press.Google Scholar
  47. Wagster, J., Tan, J., Biswas, G., & Schwartz, D. (2007). Do learning by teaching environments with metacognitive support help students develop better learning behaviors? In: D. McNamara & J. Trafton (Eds.) Proceedings of the 29th Annual Meeting of the Cognitive Science Society (pp. 695–700). Austin, TX: Cognitive Science Society.Google Scholar
  48. Wagster, J., Kwong, H., Segedy, J., Biswas, G., & Schwartz, D. (2008). Bringing CBLEs into classrooms: Experiences with the Betty's Brain system. In Proceedings of the Eighth IEEE International Conference on Advanced Learning Technologies (pp. 252–256). Santander, Cantabria, Spain.Google Scholar
  49. Willis, J., & Crowder, J. (1974). Does tutoring enhance the tutor’s academic learning? Psychology in the Schools, 11, 68–70.CrossRefGoogle Scholar

Copyright information

© International Artificial Intelligence in Education Society 2015

Authors and Affiliations

  • Gautam Biswas
    • 1
  • James R. Segedy
    • 1
  • Kritya Bunchongchit
    • 2
  1. 1.Vanderbilt UniversityNashvilleUSA
  2. 2.Mahidol UniversityBangkokThailand

Personalised recommendations