Metacognition and Learning

, Volume 2, Issue 2–3, pp 125–140 | Cite as

Designing for metacognition—applying cognitive tutor principles to the tutoring of help seeking

  • Ido RollEmail author
  • Vincent Aleven
  • Bruce M. McLaren
  • Kenneth R. Koedinger


Intelligent Tutoring Systems have been shown to be very effective in supporting learning in domains such as mathematics, physics, computer programming, etc. However, they are yet to achieve similar success in tutoring metacognition. While an increasing number of educational technology systems support productive metacognitive behavior within the scope of the system, few attempt to teach skills students need to become better future learners. To that end, we offer a set of empirically-based design principles for metacognitive tutoring. Our starting point is a set of design principles put forward by Anderson et al. (Journal of the Learning Sciences, 4:167–207, 1995) regarding Cognitive Tutors, a family of Intelligent Tutoring Systems. We evaluate the relevance of these principles to the tutoring of help-seeking skills, based on our ongoing empirical work with the Help Tutor. This auxiliary tutor agent is designed to help students learn to make effective use of the help facilities offered by a Cognitive Tutor. While most of Anderson’s principles are relevant to the tutoring of help seeking, a number of differences emerge as a result of the nature of metacognitive knowledge and of the need to combine metacognitive and domain-level tutoring. We compare our approach to other metacognitive tutoring systems, and, where appropriate, propose new guidelines to promote the discussion regarding the nature and design of metacognitive tutoring within scaffolded problem-solving environments.


Meta-cognition Help seeking Intelligent tutoring systems Cognitive tutors Instructional design principles 



We would like to thank Ryan deBaker, Eunjeong Ryu, Jo Bodnar, Ido Jamar, Brett Leber, Jonathan Sewall, Mike Konieczki, Kathy Dickensheets, Grant McKinney, Terri Murphy, Sabine Lynn, Dale Walters, Kris Hobaugh and Christy McGuire for their help carrying out these studies. This research is sponsored by NSF Award IIS-0308200, the Graduate Training Grant awarded to Carnegie Mellon University by the Department of Education (# R305B040063), and NSF Award SBE-0354420 to the Pittsburgh Sciences of Learning Center. The contents of the paper are solely the responsibility of the authors and do not necessarily represent the official views of the NSF.


  1. Aleven, V., & Koedinger, K. R. (2000). Limitations of student control: Do students know when they need help? In G. Gauthier, C. Frasson, & K. VanLehn (Eds.), Proceedings of the 5th International Conference on Intelligent Tutoring Systems (pp. 292–303). Berlin: Springer.Google Scholar
  2. Aleven, V., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26, 147–179.CrossRefGoogle Scholar
  3. Aleven, V., McLaren, B. M., Roll, I., & Koedinger, K. R. (2006). Toward meta-cognitive tutoring: A model of help seeking with a Cognitive Tutor. International Journal of Artificial Intelligence in Education, 16, 101–128.Google Scholar
  4. Aleven, V., Roll, I., McLaren, B. M., Ryu, E. J., & Koedinger, K. R. (2005). An architecture to combine meta-cognitive and cognitive tutoring: Pilot testing the Help Tutor. In Proceedings of 12th International Conference on Artificial Intelligence in Education (pp. 17–24). Amsterdam, The Netherlands: IOS.Google Scholar
  5. Aleven, V., Stahl, E., Schworm, S., Fischer, F., & Wallace, R. M. (2003). Help seeking and help design in interactive learning environments. Review of Educational Research, 73(2), 277–320.CrossRefGoogle Scholar
  6. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4(2), 167–207.CrossRefGoogle Scholar
  7. Azevedo, R. (2005a). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educational Psychologist, 40(4), 199–209.CrossRefGoogle Scholar
  8. Azevedo, R. (2005b). Computer environments as metacognitive tools for enhancing learning. Educational Psychologist, 40(4), 193–197.CrossRefGoogle Scholar
  9. Azevedo, R., Cromley, J. G., & Seibert, D. (2004). Does adaptive scaffolding facilitate students ability to regulate their learning with hypermedia? Contemporary Educational Psychology, 29, 344–370.CrossRefGoogle Scholar
  10. Baker, R. S., Corbett, A. T., Koedinger, K. R., & Wagner, A. Z. (2004). Off-task behavior in the Cognitive Tutor classroom: When students “game the system.” In ACM CHI 2004: Computer–Human Interaction (pp. 383–90).Google Scholar
  11. Baker, R. S. J. d., Corbett, A. T., Koedinger, K. R., Evenson, E., Roll, I., Wagner, A. Z., et al. (2006a). Adapting to when students game an intelligent tutoring system. In 8th International Conference on Intelligent Tutoring Systems (pp. 392–401). Berlin: Springer.Google Scholar
  12. Baker, R. S. J. d., Corbett, A. T., Koedinger, K. R., & Roll, I. (2006b). Generalizing detection of Gaming the System across a tutoring curriculum. In 8th International Conference on Intelligent Tutoring Systems (pp. 402–11). Berlin: Springer.Google Scholar
  13. Bielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995). Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. Cognition and Instruction, 13(2), 221–252.CrossRefGoogle Scholar
  14. Biswas, G., Leelawong, K., Kadira, B., Viswanath, K., Vye, N., Schwartz, D. L., et al. (2004). Incorporating self regulated learning techniques into learning by teaching environments. In The Twenty Sixth Annual Meeting of the Cognitive Science Society (pp. 120–125).Google Scholar
  15. Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.Google Scholar
  16. Brown, A. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. In F. Reiner, & R. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 65–116). Hillsdale, NJ: Erlbaum.Google Scholar
  17. Bunt, A., & Conati, C. (2003). Probabilistic student modelling to improve exploratory behavior. User Modeling and User-Adapted Interaction, 13(3), 269–309.CrossRefGoogle Scholar
  18. Carver, S. M. (2001). Cognition and instruction: Enriching the laboratory school experience of children, teachers, parents, and undergraduates. In S. M. Carver, & D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 385–426). Mahwah, NJ: Erlbaum.Google Scholar
  19. Carver, S. M., & Mayer, R. E. (1998). Learning and transfer of debugging skills: Applying task analysis to curriculum design and assessment. In Teaching and Learning Computer Programming: Multiple Research Perspectives (pp. 259–97). Hillsdale, NJ: Erlbaum.Google Scholar
  20. Conati, C., & VanLehn, K. (1999). Teaching meta-cognitive skills: Implementation and evaluation of a tutoring system to guide self-explanation while learning from examples. In Artificial Intelligence in Education (pp. 297–304). Amsterdam, The Netherlands: IOS.Google Scholar
  21. Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4, 253–278.CrossRefGoogle Scholar
  22. Corbett, A. T., & Anderson, J. R. (2001). Locus of feedback control in computer-based tutoring: Impact on learning rate, achievement and attitudes. In J. Jacko, A. Sears, M. Beaudouin-Lafon, & R. Jacob (Eds.), CHI’2001 Conference on Human Factors in Computing Systems (pp. 245–52). New York: ACM.Google Scholar
  23. de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179–201.CrossRefGoogle Scholar
  24. Del Solato, T., & du Boulay, B. (1995). Formalization and implementation of motivational tactics in tutoring systems. Journal of Artificial Intelligence in Education, 6(4), 337–378.Google Scholar
  25. Gama, C. (2004). Metacognition in interactive learning environments: The reflection assistant model. In 7th Conference on Intelligent Tutoring Systems (pp. 668–677). Berlin: Springer.Google Scholar
  26. Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30–43.Google Scholar
  27. Koedinger, K. R., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning science to the classroom. In K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (pp. 61–78). Cambridge University Press.Google Scholar
  28. Luckin, R., & Hammerton, L. (2002). 6th International Conference on Intelligent Tutoring Systems. Getting to know me: Helping learners understand their own learning needs through metacognitive scaffolding. Berlin: Springer.Google Scholar
  29. Mathan, S. A., & Koedinger, K. R. (2005). Fostering the intelligent novice: Learning from errors with metacognitive tutoring. Educational Psychologist, 40(4), 257–265.CrossRefGoogle Scholar
  30. Morgan, P., & Ritter, S. (2002). An experimental study of the effects of cognitive tutor algebra I on student knowledge and attitude. Pittsburgh, PA: Carnegie Learning, Inc.Google Scholar
  31. Quintana, C., Zhang, M., & Krajcik, J. (2005). A framework for supporting metacognitive aspects of online inquiry through software-based scaffolding. Educational Psychologist, 40(4), 235–244.CrossRefGoogle Scholar
  32. Razzaq, L., Feng, M., Nuzzo-Jones, G., Heffernan, N. T., & Koedinger, K. R. (2005). The Assistment project: Blending assessment and assisting. In The 12th International Conference on Artificial Intelligence In Education (pp. 555–62). Amsterdam: IOS.Google Scholar
  33. Reif, F., & Scott, L. A. (1999). Teaching scientific thinking skills: Students and computers coaching each other. American Journal of Physics, 67(9), 819–831.CrossRefGoogle Scholar
  34. Resnick, L. B. (1987). Education and learning to think. Washington: National Academy Press.Google Scholar
  35. Roll, I., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2007). Can help seeking be tutored? Searching for the secret sauce of metacognitive tutoring. In Proceedings of the 13th International Conference on Artificial Intelligence in Education (pp. 203–210). Amsterdam, The Netherlands: IOS.Google Scholar
  36. Roll, I., Aleven, V., McLaren, B. M., Ryu, E., Baker, R. S., & Koedinger, K. R. (2006). The Help Tutor: Does metacognitive feedback improve students’ help-seeking actions, skills and learning? In 8th International Conference in Intelligent Tutoring Systems (pp. 360–369). Berlin: Springer.Google Scholar
  37. Roll, I., Baker, R. S., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2005). Modeling students’ metacognitive errors in two intelligent tutoring systems. In user modeling 2005 (pp. 379–388). Berlin: Springer.Google Scholar
  38. Schoenfeld, A. H. (1992). Learning to think mathematically: Problem solving, metacognition, and sense-making in mathematics. In D. Grouws (Ed), Handbook of research on mathematics teaching and learning (pp. 334–370). New York: MacMillan.Google Scholar
  39. White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3–118.CrossRefGoogle Scholar
  40. Wood, H. A., & Wood, D. J. (1999). Help seeking, learning, and contingent tutoring. Computers and Education, 33(2), 153–169.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2007

Authors and Affiliations

  • Ido Roll
    • 1
    Email author
  • Vincent Aleven
    • 1
  • Bruce M. McLaren
    • 1
  • Kenneth R. Koedinger
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations