Abstract
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.
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References
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)
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)
Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive tutors: lessons learned. J. Learn. Sci. 4, 167–207 (1995)
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)
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)
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
Chi, M.T.H., de Leeuw, N., Chiu, M., LaVancher, C.: Eliciting self-explanations improves understanding. Cognit. Sci. 18, 439–477 (1994)
de Jong, T., van Joolingen, W.R.: Scientific discovery learning with computer simulations of conceptual domains. Rev. Educ. Res. 68, 179–201 (1998)
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)
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
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)
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)
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)
Kapur, M., Bielaczyc, K.: Designing for productive failure. J. Learn. Sci. 21, 45–83 (2012)
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)
Koedinger, K.R., Aleven, V.: Exploring the assistance dilemma in experiments with cognitive tutors. Educ. Psychol. Rev. 19, 239–264 (2007)
Kuhn, D., Crowell, A.: Dialogic argumentation as a vehicle for developing young adolescents’ thinking. Psychol. Sci. 22, 545–552 (2011)
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). https://link.springer.com/article/10.1007/s10648-016-9379-x
Loibl, K., Rummel, N.: Knowing what you don’t know makes failure productive. Learn. Instruct. 34, 74–85 (2014)
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)
Mayer, R.E.: Should there be a three-strikes rule against pure discovery learning? Am. Psychol. 59, 14–19 (2004)
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)
Papert S.: Mindstorms: Children, Computers, and Powerful Ideas. Basic Books, Inc., New York (1980)
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)
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)
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)
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)
Reiser, B.J.: Scaffolding complex learning: the mechanisms of structuring and problematizing student work. J. Learn. Sci. 13(3), 273–304 (2004)
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)
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
Roll, I., Holmes, N.G., Day, J., Bonn, D.: Evaluating metacognitive scaffolding in guided invention activities. Instruct. Sci. 40, 1–20 (2012)
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)
Shute, V.J., Glaser, R.: A large-scale evaluation of an intelligent discovery world: smithtown. Interact. Learn. Environ. 1, 51–77 (1990)
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)
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)
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)
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)
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The research is supported by NSF grant 1361062.
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Aleven, V., Connolly, H., Popescu, O., Marks, J., Lamnina, M., Chase, C. (2017). An Adaptive Coach for Invention Activities. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_1
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