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Encouraging Students to Study More: Adapting Feedback to Personality and Affective State

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Artificial Intelligence in Education (AIED 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6738))

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Abstract

My PhD investigates how a conversational agent can adapt feedback to the personality and affective state of learners in order to increase learner motivation. This paper provides an overview of the research area, research questions and work to date.

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References

  1. Graham, S., Weiner, B.: Theories and principles of motivation. Handbook of Educational Psychology 4, 63–84 (1996)

    Google Scholar 

  2. Thompson, E.R.: Development and validation of an international english big-five mini-markers. Personality and Individual Differences 45(6), 542–548 (2008)

    Article  Google Scholar 

  3. Blanchard, E.G., Volfson, B., Hong, Y., Lajoie, S.P.: Affective artificial intelligence in education: From detection to adaptation. In: AIED 2009, pp. 81–88 (2009)

    Google Scholar 

  4. Cooper, D.G., Muldner, K., Arroyo, I., Woolf, B.P., Burleson, W.: Ranking feature sets for emotion models used in classroom based intelligent tutoring systems. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 135–146. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  5. D’Mello, S.K., Dowell, N., Graesser, A.C.: Cohesion relationships in tutorial dialogue as predictors of affective states. In: AIED 2009, pp. 9–16 (2009)

    Google Scholar 

  6. Zhou, X., Conati, C.: Inferring user goals from personality and behavior in a causal model of user affect. In: IUI 2003, Miami, Florida, USA, pp. 211–218 (2003)

    Google Scholar 

  7. van der Sluis, I., Mellish, C.: Towards empirical evaluation of affective tactical NLG. In: Proceedings of the 12th European Workshop on NLG, pp. 146–153 (2009)

    Google Scholar 

  8. Zhang, G., Cheng, Z., He, A., Huang, T.: A WWW-based learner’s learning motivation detecting system. In: Proceedings of International Workshop on “Research Directions and Challenge Problems in Advanced Information Systems Engineering, pp. 16–19 (2003)

    Google Scholar 

  9. Mcquiggan, S.W., Mott, B.W., Lester, J.C.: Modeling self-efficacy in intelligent tutoring systems: An inductive approach. UMUAI 18(1), 81–123 (2008)

    Google Scholar 

  10. Hurley, T.: Intervention strategies to increase motivation in adaptive on-line learning. No. 1. Dublin: NCI (2006)

    Google Scholar 

  11. Bruinsma, M.: Motivation, cognitive processing and achievement in higher education. Learning and Instruction 14(6), 549–568 (2004)

    Article  Google Scholar 

  12. Du Boulay, B., Rebolledo Mendez, G., Luckin, R., Martinez-Miron, E.: Motivationally intelligent systems: Diagnosis and feedback. In: AIED 2007, pp. 563–565 (2007)

    Google Scholar 

  13. Hurley, T., Weibelzahl, S.: “MotSaRT” - motivation strategies: A recommender tool for on-line learning facilitators. In: Irish Educational Technology Users’ Conference, pp. 1–5 (2007)

    Google Scholar 

  14. Soldato, T.D., Tecnologie, I., Cnr, D., Boulay, B.D.: Implementation of motivational tactics in tutoring systems. J. of Artificial Intelligence in Education 6, 337–378 (1995)

    Google Scholar 

  15. Masthoff, J.: The user as wizard: A method for early involvement in the design and evaluation of adaptive systems. In: Fifth Workshop on User-Centred Design and Evaluation of Adaptive Systems, pp. 460–469 (2006)

    Google Scholar 

  16. Dennis, M., Masthoff, J., Pain, H., Mellish, C.: Does self-efficacy matter when generating feedback? In: Biswas, G., et al. (eds.) AIED 2011. LNCS (LNAI), vol. 6738, pp. 456–458. Springer, Heidelberg (2011)

    Google Scholar 

  17. Fogg, B.J.: Persuasive technology: Using computers to change what we think and do. Morgan Kaufmann, San Francisco (2003)

    Google Scholar 

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Dennis, M. (2011). Encouraging Students to Study More: Adapting Feedback to Personality and Affective State. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science(), vol 6738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21869-9_113

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  • DOI: https://doi.org/10.1007/978-3-642-21869-9_113

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21868-2

  • Online ISBN: 978-3-642-21869-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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