Advertisement

Core Aspects of Affective Metacognitive User Models

  • Adam Moore
  • Victoria Macarthur
  • Owen Conlan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7138)

Abstract

As user modelling moves away from a tightly integrated adjunct of adaptive systems and into user modelling service provision, it is important to consider what facets or characteristics of a user might need to be contained within a user model in order to support cognitive functions. Here we examine previous mechanisms for creating a metacognitive and affective user model. We then take first steps to describe the necessary characteristics of a user model we envisage being utilised by an affective metacognitive modelling service and make some suggestion for the source, form and content of such characteristics.

Keywords

Affect metacognition user modelling technology enhanced learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wagner, E.D.: In support of a functional definition of interaction. American Journal of Distance Education 8, 6–29 (1994)CrossRefGoogle Scholar
  2. 2.
    Conati, C.: How to Evaluate Models of User Affect? In: André, E., Dybkjær, L., Minker, W., Heisterkamp, P. (eds.) ADS 2004. LNCS (LNAI), vol. 3068, pp. 288–300. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Brusilovsky, P., Sosnovsky, S., Shcherbinina, O.: User Modeling in a Distributed E-Learning Architecture. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 387–391. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Brusilovsky, P., et al.: Learning SQL Programming with Interactive Tools: From Integration to Personalization. ACM Transactions on Computing Education 9, 1–15 (2010)CrossRefGoogle Scholar
  5. 5.
    Efklides, A.: Metacognition and affect: What can metacognitive experiences tell us about the learning process? Educational Research Review 1, 3–14 (2006)CrossRefGoogle Scholar
  6. 6.
    Craig, S., Graesser, A., Sullins, J., Gholson, B.: Affect and learning: An exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media 29, 241–250 (2004)CrossRefGoogle Scholar
  7. 7.
    Ekman, P., Friesen, W.V.: Head and body cues in the judgment of emotion: a reformulation. Perceptual and Motor Skills 24, 711–724 (1967)CrossRefGoogle Scholar
  8. 8.
    James, W.: The principles of psychology. Holt, New York (1890)CrossRefGoogle Scholar
  9. 9.
    Ekman, P.: Basic emotions. In: Dalgleish, T., Power, M. (eds.) Handbook of Cognition and Emotion, vol. 98, pp. 45–60. John Wiley & Sons (1999)Google Scholar
  10. 10.
    Flavell, J.H.: Metacognitive aspects of problem solving. In: Resnick, L.B. (ed.) The Nature of Intelligence, pp. 231–236. Erlbaum, Hillsdale (1976)Google Scholar
  11. 11.
    Brown, A.L.: Metacognitive development and reading. In: Spiro, R.J., Bruce, B., Brewer, W. (eds.) Theoretical Issues in Reading Comprehension, Hillsdale, N.J, pp. 453–482 (1980)Google Scholar
  12. 12.
    Muzio, E., Fisher, D.J., Thomas, R., Peters, V.: Soft skills quantification (SSQ) for project manager competencies. Project Management Journal 38, 30–38 (2007)Google Scholar
  13. 13.
    Vygotskiǐ, L.S., Cole, M.: Mind in society: the development of higher psychological processes. Harvard University Press, Cambridge (1978) Google Scholar
  14. 14.
    Koedinger, K.R.: Cognitive Tutors as Modeling Tool and Instructional Model. In: Forbus, K.D., Feltovich, P.J. (eds.) Smart Machines in Education: The Coming Revolution in Educational Technology, pp. 145–168. AAAI/MIT Press, Menlo Park, CA (2001)Google Scholar
  15. 15.
    Anderson, J.R.: ACT: A simple theory of complex cognition. Am. Psych. 51, 355–365 (1996)CrossRefGoogle Scholar
  16. 16.
    Anderson, J.R.: Rules of the Mind. Lawrence Erlbaum Associates, Inc., Hillsdale (1993)Google Scholar
  17. 17.
    Lesgold, A.M.: The nature and methods of learning by doing. American Psychologist 56, 964–973 (2001)CrossRefGoogle Scholar
  18. 18.
    Aleven, V., McLaren, B.M., Roll, I., Koedinger, K.R.: Toward meta-cognitive tutoring: A model of help seeking with a Cognitive Tutor. International Journal of Artificial Intelligence in Education 16, 101–130 (2006)Google Scholar
  19. 19.
    Roll, I., Aleven, V., McLaren, B.M., Koedinger, K.R.: 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 (AIED 2007), pp. 203–210. IOS Press, Amsterdam (2007)Google Scholar
  20. 20.
    Biswas, G., Leelawong, K., Schwartz, D.L., Vye, N.: Learning by Teaching: A New Agent Paradigm for Educational Software. Applied Artificial Intelligence 19, 363–392 (2005)CrossRefGoogle Scholar
  21. 21.
    Chase, C.C., Chin, D.B., Oppezzo, M.A., Schwartz, D.L.: Teachable agents and the protege effect: Increasing effort towards learning. Journal of Science Education and Technology 18, 334–352 (2009)CrossRefGoogle Scholar
  22. 22.
    Conati, C.: Intelligent Tutoring Systems: New Challenges and Directions. In: Proceedings of the 14th International Conference on Artificial Intelligence in Education (AIED), Brighton, England, pp. 2–7 (2009)Google Scholar
  23. 23.
    Azevedo, R., Witherspoon, A., Graesser, A.: MetaTutor: Analyzing Self-Regulated Learning in a Tutoring System for Biology. Artificial Intelligence in Education 200, 635–637 (2009)Google Scholar
  24. 24.
    Azevedo, R., Moos, D.C., Johnson, A.M., Chauncey, A.D.: Measuring Cognitive and Metacognitive Regulatory Processes During Hypermedia Learning: Issues and Challenges. Educational Psychologist 45, 210–223 (2010)CrossRefGoogle Scholar
  25. 25.
    Gama, C.: Metacognition in Interactive Learning Environments: The Reflection Assistant Model. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 668–677. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  26. 26.
    Tobias, S., Everson, H.T.: Knowing What You Know and What You Don’t: Further Research on Metacognitive Knowledge Monitoring, New York, USA (2002)Google Scholar
  27. 27.
    Macarthur, V., Conlan, O.: Modeling Higher-order Cognitive Skills in Technology Enhanced Distance Learning. In: 4th International Conference on Distance Learning and Education (ICDLE), pp. 15–19 (2010)Google Scholar
  28. 28.
    Schraw, G., Sperling Dennison, R.: Assessing metacognitive awareness. Contemporary Educational Psychology 19, 460–475 (1994)CrossRefGoogle Scholar
  29. 29.
    Robison, J.L., McQuiggan, S.W., Lester, J.C.: Modeling Task-Based vs. Affect-Based Feedback Behavior in Pedagogical Agents: An Inductive Approach. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence in Education, pp. 25–32 (2009)Google Scholar
  30. 30.
    D’Mello, S.K., Graesser, A.: Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User Adapted Interaction 20, 147–187 (2010)CrossRefGoogle Scholar
  31. 31.
    Neviarouskaya, A., Prendinger, H., Ishizuka, M.: User study on AffectIM, an avatar-based Instant Messaging system employing rule-based affect sensing from text. International Journal of Human-Computer Studies 68, 432–450 (2010)CrossRefGoogle Scholar
  32. 32.
    Neviarouskaya, A., Prendinger, H., Ishizuka, M.: Analysis of affect expressed through the evolving language of online communication. In: Proceedings of IUI 2007, pp. 278–281 (2007)Google Scholar
  33. 33.
    Beale, R., Creed, C.: Affective interaction: How emotional agents affect users. International Journal of Human-Computer Studies 67, 755–776 (2009)CrossRefGoogle Scholar
  34. 34.
    Girard, S., Johnson, H.: Designing Affective Computing Learning Companions with Teachers as Design Partners. In: Proceedings of the 3rd International Workshop on Affective Interaction in Natural Environments (AFFINE 2010), pp. 49–54. ACM, New York (2010)CrossRefGoogle Scholar
  35. 35.
    Lisetti, C.L., Nasoz, F.: MAUI: a multimodal affective user interface. In: Proceedings of the Tenth ACM International Conference on Multimedia, pp. 161–170. ACM (2002)Google Scholar
  36. 36.
    Pressley, M., Afflerbach, P.: Verbal Protocols of Reading: The Nature of Constructively Responsive Reading. Lawrence Erlbaum Associates, Hove (1995)Google Scholar
  37. 37.
    Ortony, A., Clore, G.L., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press (1990)Google Scholar
  38. 38.
    Dyer, M.: Emotions and their computations: Three computer models. Cognition & Emotion 1, 323–347 (1987)CrossRefGoogle Scholar
  39. 39.
    Liu, H., Lieberman, H., Selker, T.: A model of textual affect sensing using real-world knowledge. In: Proceedings of the 8th International Conference on Intelligent User Interfaces - IUI 2003, p. 125. ACM Press, New York (2003)Google Scholar
  40. 40.
    Russell, J.A.: A circumplex model of affect. J. Personality and Soc. Psych. 39, 1161–1178 (1980)CrossRefGoogle Scholar
  41. 41.
    Macarthur, V., Moore, A., Mulwa, C., Conlan, O.: Towards a Cognitive Model to Support Self-Reflection: Emulating Traits and Tasks in Higher Order Schemata. In: EC-TEL 2011 Workshop on Augmenting the Learning Experiene with Collaborative Reflection, Palermo, Sicily, Italy (2011)Google Scholar
  42. 42.
    Cattell, R.B.: Description and measurement of personality. Harcourt, Brace & World (1946)Google Scholar
  43. 43.
    Myers, I.B., McCaulley, M.H., Quenk, N.L., Hammer, A.L.: The MBTI manual. Consulting Psychologists Press (1998)Google Scholar
  44. 44.
    Ekman, P.: Facial expression and emotion. American Psychologist 48, 384–392 (1993)CrossRefGoogle Scholar
  45. 45.
    Riley-Doucet, C., Wilson, S.: A three-step method of self-reflection using reflective journal writing. Journal of Advanced Nursing 25, 964–968 (1997)CrossRefGoogle Scholar
  46. 46.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing User Modeling on Twitter for Personalized News Recommendations. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Adam Moore
    • 1
  • Victoria Macarthur
    • 1
  • Owen Conlan
    • 1
  1. 1.KDEG, School of Computer Science and StatisticsTrinity CollegeDublinRepublic of Ireland

Personalised recommendations