The MetaHistoReasoning Tool: Studying Domain-Specific Metacognitive Activities in an Intelligent Tutoring System for History

Chapter

Abstract

This chapter reviews empirical research on the MetaHistoReasoning (MHRt) tool, an intelligent tutoring system that aims to support students in regulating their own understanding of historical events in accordance with disciplinary-based practices. The design of the system is guided by a domain-specific account of the metacognitive activities involved in learning while performing inquiries into the causes of historical events. The system relies on modularization as a mechanism for delivering instruction and promoting the development of metacognitive skills. The Training Module supports skill acquisition from examples, while the Inquiry Module facilitates skill practice and refinement through problem-solving. Both modules fulfill complementary roles in skill development, since the learning outcomes for a module determines subsequent learning processes. The modular nature of the system also allows flexibility in implementing novel approaches for instruction and testing that impact towards several aspects of skill development. A pedagogical agent interacts with the learner to facilitate the transition across each module as skills become increasingly sophisticated. The aim of our research program is to improve the interactive capabilities of the agent by building assessment mechanisms that target critical aspects along this transition as a means to intervene and foster skill development. As such, we provide an overview of trace measures and analyses that are used to study how learners set goals, use strategies, and monitor the outcomes in the context of their investigations. We will review recent advances in building assessment mechanisms that target these disciplinary-based activities in order to recommend pedagogical strategies for the virtual agent embedded in the MHRt tool.

Keywords

MetaHistoReasoning tool Confusion Pedagogical agent Problem-solving Metacognitive tool Domain-specific Metacognition 

Abbreviation

MHRt

MetaHistoReasoning tool

References

  1. 1.
    Lajoie, S.P., Azevedo, R.: Teaching and learning in technology-rich learning environments. In: Alexander, P., Winne, P. (eds.) Handbook of Educational Psychology, 2nd edn, pp. 803–821. Erlbaum, Mahwah, NJ (2006)Google Scholar
  2. 2.
    Pea, R.D.: Beyond amplification: using the computer to reorganize mental functioning. Educ. Psychol. 20(4), 167–182 (1985)CrossRefGoogle Scholar
  3. 3.
    Perkins, P.R.: The fingertip effect: how information processing technology shapes thinking. Educ. Res. 14, 11–17 (1985)CrossRefGoogle Scholar
  4. 4.
    Salomon, G., Perkins, D., Globerson, T.: Partners in cognition: Extending human intelligence with intelligent technologies. Educ. Res. 20, 10–16 (1991)CrossRefGoogle Scholar
  5. 5.
    Lajoie, S.P. (ed.): Computers as Cognitive Tools vol 2: No More Walls. Erlbaum, Mahwah, NJ (2000)Google Scholar
  6. 6.
    Jonassen, D.H.: Using cognitive tools to represent problems. J. Res. Technol. Educ. 35(3), 362–381 (2003)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Lajoie, S.P.: Developing professional expertise with a cognitive apprenticeship model: Examples from avionics and medicine. In: Ericsson, K.A. (ed.) Development of Professional Expertise: Toward Measurement of Expert Performance and Design of Optimal Learning Environments, pp. 61–83. Cambridge University Press, Cambridge, UK (2009)CrossRefGoogle Scholar
  8. 8.
    Azevedo, R.: Computer environments as metacognitive tools for enhancing learning. Educ. Psychol. 40, 193–198 (2005)CrossRefGoogle Scholar
  9. 9.
    Azevedo, R.: Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educ. Psychol. 40(4), 199–209 (2005)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Azevedo, R.: The role of self-regulation in learning about science with hypermedia. In: Robinson, D., Schraw, G. (eds.) Recent Innovations in Educational Technology that Facilitate Student Learning, pp. 127–156. Information Age Publishing, Charlotte, NC (2008)Google Scholar
  11. 11.
    Azevedo, R., Aleven, V. (eds.): International Handbook of Metacognition and Learning Technologies. Springer, Berlin (2013)Google Scholar
  12. 12.
    Bjork, R., Dunlosky, J., Kornell, N.: Self-regulated learning: Beliefs, techniques, and illusions. Annu. Rev. Psychol. 64, 417–444 (2013)CrossRefGoogle Scholar
  13. 13.
    Pintrich, P.R.: A conceptual framework for assessing motivation and self-regulated learning in college students. Educ. Psychol. Rev. 16(4), 385–407 (2004)CrossRefGoogle Scholar
  14. 14.
    Schunk, D.H.: Self-regulated learning: the educational legacy of Paul R Pintrich. Educ. Psychol. 40, 85–94 (2005)CrossRefGoogle Scholar
  15. 15.
    Winne, P.H., Hadwin, A.F.: The weave of motivation and self-regulated learning. In: Schunk, D.H., Zimmerman, B.J. (eds.) Motivation and Self-Regulated Learning: Theory, Research, and Applications, pp. 297–314. Lawrence Erlbaum Associates, Mahwah, NJ (2008)Google Scholar
  16. 16.
    Zimmerman, B.J.: Investigating self-regulation and motivation: historical background, methodological developments, and future prospects. Am. Educ. Res. J. 45(1), 166–183 (2008)CrossRefGoogle Scholar
  17. 17.
    Schraw, G.: Measuring self-regulation in computer-based learning environments. Educ. Psychol. 45(4), 258–266 (2010)CrossRefGoogle Scholar
  18. 18.
    Veenman, M.V.J.: Assessing metacognitive skills in computerized learning environments. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies, pp. 157–168. Springer, New York, NY (2013)CrossRefGoogle Scholar
  19. 19.
    Winters, F.I., Greene, J.A., Costich, C.M.: Self-regulation of learning within computer-based learning environments: A critical analysis. Educ. Psychol. Rev. 20(4), 429–444 (2008)CrossRefGoogle Scholar
  20. 20.
    Tsai, C.-W., Shen, P.-D., Fan, Y.-T.: Research trends in self-regulated learning research in online learning environments: a review of studies published in selected journals from 2003 to 2012. Br. J. Educ. Technol. 44(5), E107–E110 (2013)CrossRefGoogle Scholar
  21. 21.
    Devolder, A., van Braak, J., Tondeur, J.: Supporting self-regulated learning in computer-based learning environments: systemic review of effects of scaffolding in the domain of education. J. Comput. Assist. Learn. 28(6), 557–573 (2012)CrossRefGoogle Scholar
  22. 22.
    Azevedo, R., Moos, D.C., Greene, J.A., Winters, F.I., Cromley, J.G.: Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Educ. Tech. Res. Dev. 56, 45–72 (2008)CrossRefGoogle Scholar
  23. 23.
    Greene, J.A., Bolick, C.M., Robertson, J.: Fostering historical knowledge and thinking skills using hypermedia learning environments: the role of self-regulated learning. Comput. Educ. 54, 230–243 (2010)CrossRefGoogle Scholar
  24. 24.
    Poitras, E.P., Lajoie, S.P., Hong, Y.J.: The design of technology-rich learning environments as metacognitive tools in history education. Instr. Sci. 40, 1033–1061 (2012)CrossRefGoogle Scholar
  25. 25.
    Azevedo, R., Feyzi-Behnagh, R.: Dysregulated learning with advanced learning technologies. J. e-Learn. Know. Soc. 7(2), 9–18 (2011)Google Scholar
  26. 26.
    Linderholm, T., Everson, M., van den Broek, P., Mischinski, M., Crittenden, A., Samuels, J.: Effects of causal text revisions on more- and less-skilled readers’ comprehension of easy and difficult texts. Cogn. Instr. 18, 525–556 (2000)CrossRefGoogle Scholar
  27. 27.
    Gilabert, R., Martinez, G., Vidal-Abarca, E.: Some good texts are always better: Text revision to foster inferences of readers with high and low prior background knowledge. Learn. Instr. 15(1), 45–68 (2005)CrossRefGoogle Scholar
  28. 28.
    D’Mello, S., Lehman, B., Pekrun, R., Graesser, A.C.: Confusion can be beneficial for learning. Learn. Instr. 29, 153–170 (2014)CrossRefGoogle Scholar
  29. 29.
    Lehman, B., D’Mello, S.K., Graesser, A.C.: Confusion and complex learning during interactions with computer learning environments. Int High. Educ. 15, 184–194 (2012)CrossRefGoogle Scholar
  30. 30.
    Poitras, E., Lajoie, S.P.: A three-pronged approach to the design of technology-rich learning environments. In: Atkinson, R. (ed.) Learning Environments: Technologies, Challenges and Impact Assessment. Nova Science Publishers Inc., Hauppauge, NY (2013)Google Scholar
  31. 31.
    Pintrich, P.R.: Multiple goals, multiple pathways: the role of goal orientations in learning and achievement. J. Educ. Psychol. 92, 544–555 (2000)CrossRefGoogle Scholar
  32. 32.
    Zimmerman, B.J.: Theories of self-regulated learning and academic achievement: An overview and analysis. In: Zimmerman, B.J., Schunk, D.H. (eds.) Self-Regulated Learning and Academic Achievement: Theoretical Perspectives, 2nd edn, pp. 1–37. Erlbaum, Mahwah, NJ (2001)Google Scholar
  33. 33.
    Alexander, P., Dinsmore, D., Parkinson, M., Winters, F.: Self-regulated learning in academic domains. In: Zimmerman, B.J., Schunk, D.H. (eds.) Handbook of Self-Regulation of Learning and Performance, pp. 393–407. Routledge, New York, NY (2011)Google Scholar
  34. 34.
    Poitras, E., Lajoie, S.P.: A domain-specific account of self-regulated learning: the cognitive and metacognitive activities involved in learning through historical inquiry. Metacogn. & Learn. 8(3), 213–234 (2013)CrossRefGoogle Scholar
  35. 35.
    Carretero, M., López-Manjón, A., Jacott, L.: Explanining historical events. Int. J. Educ. Res. 27(3), 245–253 (1997)CrossRefGoogle Scholar
  36. 36.
    Nokes, J.D., Dole, J.A., Hacker, D.J.: Teaching high school students to use heuristics while reading historical texts. J. Educ. Psychol. 99(3), 492–504 (2007)CrossRefGoogle Scholar
  37. 37.
    van Drie, J., van Boxtel, C.: Historical reasoning: Towards a framework for analyzing students’ reasoning about the past. Educ. Psychology Rev. 20(2), 87–110 (2008)CrossRefGoogle Scholar
  38. 38.
    Winne, P.H.: A perspective on state-of-the-art research on self-regulated learning. Instr. Sci. 33, 559–565 (2005)CrossRefGoogle Scholar
  39. 39.
    Winne, P.H.: A cognitive and metacognitive analysis of self-regulated learning. In: Zimmerman, B.J., Schunk, D.H. (eds.) Handbook of Self-Regulation of Learning and Performance, pp. 15–32. Routledge, New York, NY (2011)Google Scholar
  40. 40.
    Winne, P.H., Hadwin, A.F.: Self-regulated learning and sociocognitive theory. In: Peterson, P., Baker, E., McGraw, B. (eds.) International Encyclopedia of Education, vol. 5, pp. 503–508. Elsevier, Amsterdam (2010)CrossRefGoogle Scholar
  41. 41.
    Voss, J.F., Wiley, J.: Developing understanding while writing essays in history. Int. J. Educ. Res. 27(3), 255–265 (1997)CrossRefGoogle Scholar
  42. 42.
    Renkl, A.: Instruction based on examples. In: Mayer, R.E., Alexander, P.A. (eds.) Handbook of research on learning and instruction. Routledge, New York, NY (2010)Google Scholar
  43. 43.
    Renkl, A., Hilbert, T., Schworm, S.: Example-based learning in heuristic domains: A cognitive load theory account. Educ. Psychol. Rev. 21(1), 67–78 (2009)CrossRefGoogle Scholar
  44. 44.
    Hmelo-Silver, C.E., Duncan, R.G., Chinn, C.A.: Scaffolding and achievement in problem-based learning and inquiry learning: a response to Kirschner, Sweller, and Clark (2006). Educ. Psychol. 42(2), 99–107 (2007)CrossRefGoogle Scholar
  45. 45.
    Krajcik, J.S., Blumenfeld, P.: Project-based learning. In: Sawyer, R.K. (ed.) The Cambridge Handbook of the Learning Sciences, pp. 317–334. New York, NY, Cambridge (2006)Google Scholar
  46. 46.
    Levstik, L.S.: Learning history. In: Mayer, R.E., Alexander, P.A. (eds.) Handbook of Research on Learning and Instruction, pp. 108–126. Routledge, New York, NY (2011)Google Scholar
  47. 47.
    Loyens, S.M.M., Rikers, R.M.J.P.: Instruction based on inquiry. In: Mayer, R., Alexander, P. (eds.) Handbook of Research on Learning and Instruction. Routledge, New York, NY (2011)Google Scholar
  48. 48.
    Gong, Y., Beck, J.E., Heffernan, N.T.: Comparing knowledge tracing and performance factor analysis by using multiple model fitting procedure. In: Aleven, V., Kay, J., Mostow, J. (eds.) Intelligent Tutoring Systems, pp. 35–44. Springer, Berlin Heidelberg (2010)CrossRefGoogle Scholar
  49. 49.
    Martin, B., Mitrovic, T., Mathan, S., Koedinger, K.R.: Evaluating and improving adaptive educational systems with learning curves. User Model. User-Adap. Inter. 21, 249–283 (2011)CrossRefGoogle Scholar
  50. 50.
    Lajoie, S.P.: Transitions and trajectories for studies of expertise. Educ. Res. 32(8), 21–25 (2003)CrossRefGoogle Scholar
  51. 51.
    Bull, S., Kay, J.: Student models that invite the learner in: the SMILI:() open learner modelling framework. Int. J. Arti. Intell. Educ., 17(2), 89–120 (2007)Google Scholar
  52. 52.
    Dimitrova, V., McCalla, G., Bull, S.: Open learner models: future research directions. Special Issue of the IJAAIED (Part 2). Int. J. Arti. Intell. Educ. 17(3), 217–226 (2007)Google Scholar
  53. 53.
    Shute, V.J., Zapata-Rivera, D.: Adaptive educational systems. In: Durlach, P. (ed.) Adaptive technologies for training and education, pp. 7–27. Cambridge University Press, New York NY (2012)CrossRefGoogle Scholar
  54. 54.
    Segedy, J.R., Biswas, G., Sulcer, B.: A model-based behavior analysis approach for open-ended environments. J. Educ. Technol. Soc. 17(1), 272–282 (2014)Google Scholar
  55. 55.
    Azevedo, R., Johnson, A., Chauncey, A., Burkett, C.: Self-regulated learning with MetaTutor: advancing the science of learning with MetaCognitive tools. In: Khine, M., Saleh, I. (eds.) New Science of Learning: Computers, Cognition, and Collaboration in Education, pp. 225–247. Springer, Amsterdam (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Advanced Instructional Systems and Technologies LaboratoryUniversity of Utah Educational PsychologySalt Lake CityUSA

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