An Adaptive Coach for Invention Activities

  • Vincent Aleven
  • Helena Connolly
  • Octav Popescu
  • Jenna Marks
  • Marianna Lamnina
  • Catherine Chase
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10331)


A focus in recent AIED research is to create adaptive support for learners in inquiry learning environments. However, only few examples of such support have been demonstrated. Our work focuses on Invention activities, inquiry activities in which students generate representations that explain data presented as contrasting cases. To help teachers implement these activities in their classrooms, we have created and pilot-tested a dedicated adaptive computer coach (the Invention Coach) and are currently evaluating it in a classroom study. The Coach’s pedagogical strategy balances structuring and problematizing, unlike many ITSs, which favor structuring. The Coach is implemented in CTAT as a model-tracing tutor, with a rule-based model that captures its pedagogical coaching strategy, designed in part based on data from human tutors. We describe the Invention Coach and its pedagogical model. We present evidence from our pilot tests that illustrate the tutor’s versatility and provide preliminary evidence of its effectiveness. The contributions of the work are: identifying an adaptive coaching strategy for Invention tasks that balances structuring and problematizing, and an automated coach for a successful instructional method (Invention) for which few tutors have been built.


Invention Adaptive coach Intelligent tutoring system STEM education Productive failure Inquiry learning 



The research is supported by NSF grant 1361062.


  1. 1.
    Aleven, V.: Rule-based cognitive modeling for intelligent tutoring systems. In: Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds.) Advances in Intelligent Tutoring Systems, pp. 33–62. Springer, Berlin, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Aleven, V., McLaren, B.M., Sewall, J., van Velsen, M., et al.: Example-tracing tutors: intelligent tutor development for non-programmers. Int. J. Artif. Intell. Educ. 26, 224–269 (2016)CrossRefGoogle Scholar
  3. 3.
    Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive tutors: lessons learned. J. Learn. Sci. 4, 167–207 (1995)CrossRefGoogle Scholar
  4. 4.
    Bransford, J.D., Franks, J.J., Vye, N.J., Sherwood, R.D.: New approaches to instruction: because wisdom can’t be told. In: Vosniadou, S., Ortony, A. (eds.) Similarity and Analogical Reasoning, pp. 470–497. Cambridge University Press, New York (1989)Google Scholar
  5. 5.
    Chase, C., Marks, J., Bernett, D., Aleven, V.: The design of an exploratory learning environment to support invention. In: Proceedings, Workshop on Intelligent Support in Exploratory and Open-Ended Learning Environments, held during AIED 2015 (2015)Google Scholar
  6. 6.
    Chase, C.C., Marks, J., Bernett, D., Bradley, M., Aleven, V.: Towards the development of the invention coach: a naturalistic study of teacher guidance for an exploratory learning task. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds.) AIED 2015. LNCS (LNAI), vol. 9112, pp. 558–561. Springer, Cham (2015). doi: 10.1007/978-3-319-19773-9_61 CrossRefGoogle Scholar
  7. 7.
    Chi, M.T.H., de Leeuw, N., Chiu, M., LaVancher, C.: Eliciting self-explanations improves understanding. Cognit. Sci. 18, 439–477 (1994)Google Scholar
  8. 8.
    de Jong, T., van Joolingen, W.R.: Scientific discovery learning with computer simulations of conceptual domains. Rev. Educ. Res. 68, 179–201 (1998)CrossRefGoogle Scholar
  9. 9.
    Donnelly, D.F., Linn, M.C., Ludvigsen, S.: Impacts and characteristics of computer-based science inquiry learning environments for precollege students. Rev. Educ. Res. 84, 572–608 (2014)CrossRefGoogle Scholar
  10. 10.
    Dragon, T., Arroyo, I., Woolf, B.P., Burleson, W., Kaliouby, R., Eydgahi, H.: Viewing student affect and learning through classroom observation and physical sensors. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 29–39. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-69132-7_8 CrossRefGoogle Scholar
  11. 11.
    Gobert, J.D., Sao Pedro, M., Raziuddin, J., Baker, R.S.: From log files to assessment metrics: measuring students’ science inquiry skills using educational data mining. J. Learn. Sci. 22, 521–563 (2013)CrossRefGoogle Scholar
  12. 12.
    Heffernan, N.T., Koedinger, K.R., Razzaq, L.: Expanding the model-tracing architecture: a 3rd generation intelligent tutor for Algebra symbolization. Int. J. Artif. Intell. Educ. 18, 153–178 (2008)Google Scholar
  13. 13.
    Hmelo-Silver, C.E., Duncan, R.G., Chinn, C.A.: Scaffolding and achievement in problem-based and inquiry learning: a response to Kirschner, Sweller, and Clark (2006). Educ. Psychol. 42, 99–107 (2007)CrossRefGoogle Scholar
  14. 14.
    Kapur, M., Bielaczyc, K.: Designing for productive failure. J. Learn. Sci. 21, 45–83 (2012)CrossRefGoogle Scholar
  15. 15.
    Kirschner, P.A., Sweller, J., Clark, R.E.: Why minimal guidance during instruction does not work: an analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educ. Psychol. 41, 75–86 (2006)CrossRefGoogle Scholar
  16. 16.
    Koedinger, K.R., Aleven, V.: Exploring the assistance dilemma in experiments with cognitive tutors. Educ. Psychol. Rev. 19, 239–264 (2007)CrossRefGoogle Scholar
  17. 17.
    Kuhn, D., Crowell, A.: Dialogic argumentation as a vehicle for developing young adolescents’ thinking. Psychol. Sci. 22, 545–552 (2011)CrossRefGoogle Scholar
  18. 18.
    Loibl, K., Roll, I., Rummel, N.: Towards a theory of when and how problem solving followed by instruction supports learning. Educ. Psychol. Rev., 1–23 (2016).
  19. 19.
    Loibl, K., Rummel, N.: Knowing what you don’t know makes failure productive. Learn. Instruct. 34, 74–85 (2014)CrossRefGoogle Scholar
  20. 20.
    Marks, J., Bernett, D., Chase, C.C.: The invention coach: integrating data and theory in the design of an exploratory learning environment. Int. J. Des. Learn. 7, 74–92 (2016)Google Scholar
  21. 21.
    Mayer, R.E.: Should there be a three-strikes rule against pure discovery learning? Am. Psychol. 59, 14–19 (2004)CrossRefGoogle Scholar
  22. 22.
    Mitrovic, A.: Fifteen years of constraint-based tutors: what we have achieved and where we are going. User Model. User Adapt. Interact. 22, 39–72 (2011)CrossRefGoogle Scholar
  23. 23.
    Papert S.: Mindstorms: Children, Computers, and Powerful Ideas. Basic Books, Inc., New York (1980)Google Scholar
  24. 24.
    Pea, R.D.: The social and technological dimensions of scaffolding and related theoretical concepts for learning, education, and human activity. J. Learn. Sci. 13, 423–451 (2004)CrossRefGoogle Scholar
  25. 25.
    Poitras, E.G., Lajoie, S.P.: Developing an agent-based adaptive system for scaffolding self-regulated inquiry learning in history education. Educ. Technol. Res. Dev. 62, 335–366 (2014)CrossRefGoogle Scholar
  26. 26.
    Puntambekar, S., Hubscher, R.: Tools for scaffolding students in a complex learning environment: what have we gained and what have we missed? Educ. Psychol. 40, 1–12 (2005)CrossRefGoogle Scholar
  27. 27.
    Quintana, C., Reiser, B.J., Davis, E.A., Krajcik, J., et al.: A scaffolding design framework for software to support science inquiry. J. Learn. Sci. 13, 337–386 (2004)CrossRefGoogle Scholar
  28. 28.
    Reiser, B.J.: Scaffolding complex learning: the mechanisms of structuring and problematizing student work. J. Learn. Sci. 13(3), 273–304 (2004)CrossRefGoogle Scholar
  29. 29.
    Roll, I., Aleven, V., Koedinger, K.R.: Helping students know ‘further’—increasing the flexibility of students’ knowledge using symbolic invention tasks. In: Taatgen, N.A., van Rijn H. (eds.), Proceedings (CogSci 2009), pp. 1169–1174. Cognitive Science Society, Austin (2009)Google Scholar
  30. 30.
    Roll, I., Aleven, V., Koedinger, K.R.: The invention lab: using a hybrid of model tracing and constraint-based modeling to offer intelligent support in inquiry environments. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6094, pp. 115–124. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-13388-6_16 CrossRefGoogle Scholar
  31. 31.
    Roll, I., Holmes, N.G., Day, J., Bonn, D.: Evaluating metacognitive scaffolding in guided invention activities. Instruct. Sci. 40, 1–20 (2012)CrossRefGoogle Scholar
  32. 32.
    Shemwell, J.T., Chase, C.C., Schwartz, D.L.: Seeking the general explanation: a test of inductive activities for learning and transfer. J. Res. Sci. Teach. 52, 58–83 (2015)CrossRefGoogle Scholar
  33. 33.
    Shute, V.J., Glaser, R.: A large-scale evaluation of an intelligent discovery world: smithtown. Interact. Learn. Environ. 1, 51–77 (1990)CrossRefGoogle Scholar
  34. 34.
    Schwartz, D.L., Martin, T.: Inventing to prepare for future learning: the hidden efficiency of encouraging original student production in statistics instruction. Cognit. Instruct. 22, 129–184 (2004)CrossRefGoogle Scholar
  35. 35.
    Schwartz, D.L., Chase, C.C., Oppezzo, M.A., Chin, D.B.: Practicing versus inventing with contrasting cases: the effects of telling first on learning and transfer. J. Educ. Psychol. 103, 759–775 (2011)CrossRefGoogle Scholar
  36. 36.
    VanLehn, K., Lynch, C., Schulze, K., Shapiro, J.A., et al.: The Andes physics tutoring system: lessons learned. Int. J. Artif. Intell. Educ. 15, 147–204 (2005)Google Scholar
  37. 37.
    Xun, G.E., Land, S.M.: A conceptual framework for scaffolding III-structured problem-solving processes using question prompts and peer interactions. Educ. Technol. Res. Dev. 52, 5–22 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Vincent Aleven
    • 1
  • Helena Connolly
    • 2
  • Octav Popescu
    • 1
  • Jenna Marks
    • 2
  • Marianna Lamnina
    • 2
  • Catherine Chase
    • 2
  1. 1.Human Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.Teachers CollegeColumbia UniversityNew YorkUSA

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