Advertisement

Identifying Learning Conditions that Minimize Mind Wandering by Modeling Individual Attributes

  • Kristopher Kopp
  • Robert Bixler
  • Sidney D’Mello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)

Abstract

The propensity to involuntarily disengage by zoning out or mind wandering (MW) is a common phenomenon that has negative effects on learning. The ability to stay focused while learning from instructional texts involves factors related to the text, to the task, and to the individual. This study explored the possibility that learners could be placed in optimal conditions (task and text) to reduce MW based on an analysis of individual attributes. Students studied four texts which varied along dimensions of value and difficulty while reporting instances of MW. Supervised machine learning techniques based on a small set of individual difference attributes determined the optimal condition for each participant with some success when considering value and difficulty separately (kappas of .16 and .24; accuracy of 59% and 64% respectively). Results are discussed in terms of creating a learning system that prospectively places learners in the optimal condition to increase learning by minimizing MW.

Keywords

engagement mind wandering affect machine learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Calvo, R.A., D’Mello, S.K.: Frontiers of affect-aware learning technologies. IEEE Intelligent Systems 27(6), 86–89 (2012)CrossRefGoogle Scholar
  2. 2.
    D’Mello, S.K.: A Selective Meta-analysis on the Relative Incidence of Discrete Affective States during Learning with Technology. Journal of Educational Psychology (2013)Google Scholar
  3. 3.
    D’Mello, S.K., Graesser, A.C.: Feeling, Thinking, and Computing with Affect-Aware Learning Technologies. In: Calvo, R.A., D’Mello, S.K., Gratch, J., Kappas, A. (eds.) Handbook of Affective Computing. Oxford University Press (in press)Google Scholar
  4. 4.
    Picard, R.W.: Affective Computing. MIT Press, Cambridge (1997)Google Scholar
  5. 5.
    Graesser, A.C., Olney, A., Haynes, B.C., Chipman, P.: AutoTutor: A cognitive system that simulates a tutor that facilitates learning through mixed-initiative dialogue. In: Forsythe, C., Bernard, M.L., Goldsmith, T.E. (eds.) Cognitive Systems: Human Cognitive Models in Systems Design. Erlbaum, Mahwah (2005)Google Scholar
  6. 6.
    VanLehn, K., Graesser, A.C., Jackson, G.T., Jordan, P., Olney, A., Rosé, C.P.: When are tutorial dialogues more effective than reading? Cognitive Science 31(1), 3–62 (2007)CrossRefGoogle Scholar
  7. 7.
    Baker, R., D’Mello, S., Rodrigo, M., Graesser, A.: Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies 68(4), 223–241 (2010)CrossRefGoogle Scholar
  8. 8.
    D’Mello, S., Olney, A., Williams, C., Hays, P.: Gaze tutor: A gaze-reactive intelligent tutoring system. International Journal of human-computer studies 70(5), 377–398 (2012)CrossRefGoogle Scholar
  9. 9.
    Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., Picard, R.: Affect-aware tutors: Recognizing and responding to student affect. International Journal of Learning Technology 4(3/4), 129–163 (2009)CrossRefGoogle Scholar
  10. 10.
    Mooneyham, B.W., Schooler, J.W.: The costs and benefits of mind-wandering: A review. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale 67(1), 11 (2013)CrossRefGoogle Scholar
  11. 11.
    Szpunar, K.K., Moulton, S.T., Schacter, D.L.: Mind Wandering and education: From the classroom to online learning. Frontiers in Psychology, 4 (2013)Google Scholar
  12. 12.
    Ekman, P.: An argument for basic emotions. Cognition & Emotion 6(3-4), 169–200 (1992)CrossRefGoogle Scholar
  13. 13.
    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(3), 241–250 (2004)CrossRefGoogle Scholar
  14. 14.
    Baker, R.S.J.d., Gowda, S.M., Wixon, M., Kalka, J., Wagner, A.Z., Salvi, A., Aleven, V., Kusbit, G., Ocumpaugh, J., Rossi, L.: Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra. In: Proceedings of the 5th International Conference on Educational Data Mining, pp. 126–133 (2012)Google Scholar
  15. 15.
    Conati, C., Maclaren, H.: Empirically building and evaluating a probabilistic model of user affect. User Modeling and User-Adapted Interaction 19(3), 267–303 (2009)CrossRefGoogle Scholar
  16. 16.
    D’Mello, S.K., Jackson, G.T., Craig, S.D., Morgan, B., Chipman, P., White, H., Person, N., Kort, B., el Kaliouby, R., Picard, R., Graesser, A.C.: AutoTutor detects and responds to learners affective and cognitive states. In: Workshop on Emotional and Cognitive Issues at the International Conference on Intelligent Tutoring Systems (2008)Google Scholar
  17. 17.
    Drummond, J., Litman, D.: In the Zone: Towards Detecting Student Zoning Out Using Supervised Machine Learning. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part II. LNCS, vol. 6095, pp. 306–308. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Fredricks, J.A., Blumenfeld, P.C., Paris, A.H.: School engagement: Potential of the concept, state of the evidence. Review of Educational Research 74(1), 59–109 (2004)CrossRefGoogle Scholar
  19. 19.
    Pekrun, R., Linnenbrink-Garcia, L.: Academic emotions and student engagement. In: Handbook of Research on Student Engagement, pp. 259–282. Springer, US (2012)CrossRefGoogle Scholar
  20. 20.
    Gregory, A., Allen, J.P., Mikami, A.Y., Hafen, C.A., Pianta, R.C.: Effects of a professional development program on behavioral engagement of students in middle and high school. Psychology in the Schools 51(2), 143–163 (2014)CrossRefGoogle Scholar
  21. 21.
    Snow, C.: Reading for understanding: Toward an R&D program in reading comprehension. RAND Corporation, Santa Monica (2002)Google Scholar
  22. 22.
    Mills, C., D’Mello, S.K.: How Do Extrinsic Value and Difficulty Impact Engagement: An Experimental Approach (in prep.)Google Scholar
  23. 23.
    Brown, J.I.: The Nelson-Denny Reading Test (1960)Google Scholar
  24. 24.
    Daneman, M., Carpenter, P.A.: Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior 19(4), 450–466 (1980)CrossRefGoogle Scholar
  25. 25.
    Linnenbrink-Garcia, L., Durik, A.M., Conley, A.M., Barron, K.E., Tauer, J.M., Karabenick, S.A., Harackiewicz, J.M.: Measuring Situational Interest in Academic Domains. Educational and Psychological Measurement 70(4), 647–671 (2010)CrossRefGoogle Scholar
  26. 26.
    Farmer, R., Sundberg, N.D.: Boredom proneness–the development and correlates of a new scale. Journal of Personality Assessment 50(1), 4–17 (1986)CrossRefGoogle Scholar
  27. 27.
    Acee, T.W., Kim, H., Kim, H.J., Kim, J.I., Chu, H.N.R., Kim, M., Cho, Y., Wicker, F.W.: Academic boredom in under-and over-challenging situations. Contemporary Educational Psychology 35(1), 17–27 (2010)CrossRefGoogle Scholar
  28. 28.
    Feng, S., D’Mello, S., Graesser, A.C.: Mind Wandering while reading easy and difficult texts. Psychonomic Bulletin & Review, 1–7 (2013)Google Scholar
  29. 29.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)CrossRefGoogle Scholar
  30. 30.
    Kononenko, I.: Estimating attributes: Analysis and extensions of RELIEF. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kristopher Kopp
    • 1
  • Robert Bixler
    • 2
  • Sidney D’Mello
    • 1
    • 2
  1. 1.Department of PsychologyUniversity of Notre DameSouth BendUSA
  2. 2.Computer ScienceUniversity of Notre DameSouth BendUSA

Personalised recommendations