Skip to main content
SpringerLink
Log in

Reducing Mind-Wandering During Vicarious Learning from an Intelligent Tutoring System

  • Conference paper
  • First Online:
Artificial Intelligence in Education (AIED 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11625))

Included in the following conference series:

Abstract

Mind-wandering is a ubiquitous phenomenon that is negatively related to learning. The purpose of the current study is to examine mind-wandering during vicarious learning, where participants observed another student engage in a learning session with an intelligent tutoring system (ITS). Participants (N = 118) watched a prerecorded learning session with GuruTutor, a dialogue-based ITS for biology. The response accuracy of the student interacting with the tutor (i.e., the firsthand student) was manipulated across three conditions: Correct (100% accurate responses), Incorrect (0% accurate), and Mixed (50% accurate). Results indicated that Firsthand Student Expertise influenced the frequency of mind-wandering in the participants who engaged vicariously (secondhand students), such that viewing a moderately-skilled firsthand learner (Mixed correctness) reduced the rate of mind-wandering (M = 25.4%) compared to the Correct (M = 33.9%) and Incorrect conditions (M = 35.6%). Firsthand Student Expertise did not impact learning, and we also found no evidence of an indirect effect of Firsthand Student Expertise on learning through mind-wandering (Firsthand Student Expertise → Mind-wandering → Learning). Our findings provide evidence that mind-wandering is a frequent experience during online vicarious learning and offer initial suggestions for the design of vicarious learning experiences that aim to maintain learners’ attentional focus.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Craig, S.D., Driscoll, D.M., Gholson, B.: Constructing knowledge from dialog in an intelligent tutoring system: Interactive learning, vicarious learning, and pedagogical agents. J. Educ. Multimedia Hypermedia 13, 163 (2004)

    Google Scholar 

  2. Gholson, B., Craig, S.D.: Promoting constructive activities that support vicarious learning during computer-based instruction. Educ. Psychol. Rev. 18, 119–139 (2006)

    Article  Google Scholar 

  3. Driscoll, D.M., Craig, S.D., Gholson, B., et al.: Vicarious learning: effects of overhearing dialog and monologue-like discourse in a virtual tutoring session. J. Educ. Comput. Res. 29, 431–450 (2003)

    Article  Google Scholar 

  4. Chi, M.T.H., Kang, S., Yaghmourian, D.L.: Why students learn more from dialogue- than monologue-videos: analyses of peer interactions. J. Learn. Sci. 26, 10–50 (2017)

    Article  Google Scholar 

  5. Chi, M.T.H., Roy, M., Hausmann, R.G.M.: Observing tutorial dialogues collaboratively: insights about human tutoring effectiveness from vicarious learning. Cogn. Sci. 32, 301–341 (2008)

    Article  Google Scholar 

  6. Cox, R., McKendree, J., Tobin, R., et al.: Vicarious learning from dialogue and discourse. Instr. Sci. 27, 431–458 (1999)

    Google Scholar 

  7. Craig, S.D., Sullins, J., Witherspoon, A., Gholson, B.: The deep-level-reasoning-question effect: the role of dialogue and deep-level-reasoning questions during vicarious learning. Cogn. Instr. 24, 565–591 (2006)

    Article  Google Scholar 

  8. Tree, J.E.F.: Listening in on monologues and dialogues. Discourse Processes 27, 35–53 (1999)

    Article  Google Scholar 

  9. Twyford, J., Craig, S.D.: Modeling goal setting within a multimedia environment on complex physics content. J. Educ. Comput. Res. 55, 374–394 (2017)

    Article  Google Scholar 

  10. Tree, J.E.F., Mayer, S.A.: Overhearing single and multiple perspectives. Discourse Processes 45, 160–179 (2008)

    Article  Google Scholar 

  11. Chi, M., Wylie, R.: The ICAP framework: linking cognitive engagement to active learning outcomes. Educ. Psychol. 49, 219–243 (2014)

    Article  Google Scholar 

  12. Olney, A.M., Risko, E.F., D’Mello, S.K., Graesser, A.C.: Attention in educational contexts: the role of the learning task in guiding attention. In: Fawcett, J.M., Risko, E.F., Kingstone, A., et al. (eds.) The Handbook of Attention, pp. 623–641. MIT Press, Cambridge (2015)

    Google Scholar 

  13. Risko, E.F., Anderson, N., Sarwal, A., et al.: Everyday attention: variation in mind wandering and memory in a lecture. Appl. Cogn. Psychol. 26, 234–242 (2012)

    Article  Google Scholar 

  14. Hutt, S., Mills, C., Bosch, N., et al.: “Out of the fr-eye-ing pan”: towards gaze-based models of attention during learning with technology in the classroom. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 94–103. ACM, New York (2017)

    Google Scholar 

  15. Hutt, S., Mills, C., White, S., et al.: The eyes have it: Gaze-based detection of mind wandering during learning with an intelligent tutoring system. In: Proceedings of the 9th International Conference on Educational Data Mining, International Educational Data Mining Society, EDM, pp. 86–93 (2016)

    Google Scholar 

  16. Mills, C., D’Mello, S., Bosch, N., Olney, Andrew M.: Mind wandering during learning with an intelligent tutoring system. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M.F. (eds.) AIED 2015. LNCS (LNAI), vol. 9112, pp. 267–276. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19773-9_27

    Chapter  Google Scholar 

  17. Adams, D.M., McLaren, B.M., Durkin, K., et al.: Using erroneous examples to improve mathematics learning with a web-based tutoring system. Comput. Hum. Behav. 36, 401–411 (2014)

    Article  Google Scholar 

  18. Tsovaltzi, D., Melis, E., McLaren, B.M., Meyer, A.-K., Dietrich, M., Goguadze, G.: Learning from erroneous examples: when and how do students benefit from them? In: Wolpers, M., Kirschner, Paul A., Scheffel, M., Lindstaedt, S., Dimitrova, V. (eds.) EC-TEL 2010. LNCS, vol. 6383, pp. 357–373. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16020-2_24

    Chapter  Google Scholar 

  19. D’Mello, S.K.: What do we think about when we learn? In: Millis, K., Magliano, J., Long, D.L., Weimer, K. (eds.) Understanding Deep Learning, Educational Technologies and Deep Learning, and Assessing Deep Learning, pp. 52–67. Routledge/Taylor and Francis (2018)

    Google Scholar 

  20. Olney, A., Person, N.K., Graesser, A.C.: Guru: designing a conversational expert intelligent tutoring system. In: Boonthum-Denecke, C., McCarthy, P., Lamkin, T. (eds.) Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches, pp. 156–171. IGI Global, Hershey (2012)

    Chapter  Google Scholar 

  21. Olney, Andrew M., et al.: Guru: a computer tutor that models expert human tutors. In: Cerri, Stefano A., Clancey, William J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 256–261. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30950-2_32

    Chapter  Google Scholar 

  22. Person, N.K., Olney, A., D’Mello, S.K., Lehman, B.: Interactive concept maps and learning outcomes in guru. In: Florida Artificial Intelligence Research Society (FLAIRS) Conference, pp. 456-461. AAAI Press (2012)

    Google Scholar 

  23. D’Mello, S., Hays, P., Williams, C., Cade, W., Brown, J., Olney, A.: Collaborative lecturing by human and computer tutors. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6095, pp. 178–187. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13437-1_18

    Chapter  Google Scholar 

  24. Mason, W., Suri, S.: Conducting behavioral research on Amazon’s Mechanical Turk. Behav. Res. Methods 44, 1–23 (2012)

    Article  Google Scholar 

  25. Rand, D.G.: The promise of Mechanical Turk: how online labor markets can help theorists run behavioral experiments. J. Theor. Biol. 299, 172–179 (2012)

    Article  MathSciNet  Google Scholar 

  26. Sprouse, J.: A validation of Amazon Mechanical Turk for the collection of acceptability judgments in linguistic theory. Behav. Res. Methods 43, 155–167 (2011)

    Article  Google Scholar 

  27. Mills, C., Fridman, I., Soussou, W., et al.: Put your thinking cap on: detecting cognitive load using EEG during learning. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 80–89. ACM (2017)

    Google Scholar 

  28. Mills, C., Graesser, A., Risko, E.F., D’Mello, S.K.: Cognitive coupling during reading. J. Exp. Psychol. Gen. 146, 872–883 (2017)

    Article  Google Scholar 

  29. Zhao, X., Lynch Jr., J.G., Chen, Q.: Reconsidering Baron and Kenny: Myths and truths about mediation analysis. J. Consum. Res. 37, 197–206 (2010)

    Article  Google Scholar 

  30. Tingley, D., Yamamoto, T., Hirose, K., et al.: Mediation: R package for causal mediation analysis UCLA Statistics/American Statistical Association, pp. 1–40 (2014)

    Google Scholar 

  31. Risko, E.F., Buchanan, D., Medimorec, S., Kingstone, A.: Everyday attention: mind wandering and computer use during lectures. Comput. Educ. 68, 275–283 (2013)

    Article  Google Scholar 

  32. Seli, P., Carriere, J.S., Wammes, J.D., et al.: On the clock: evidence for the rapid and strategic modulation of mind wandering. Psychol. Sci. 29, 1247–1256 (2018)

    Article  Google Scholar 

  33. Muller, D.A., Bewes, J., Sharma, M.D.: Reimann P Saying the wrong thing: improving learning with multimedia by including misconceptions. J. Comput. Assist. Learn. 24, 144–155 (2008)

    Article  Google Scholar 

  34. Bixler, R., D’Mello, S.: Automatic gaze-based user-independent detection of mind wandering during computerized reading. User Model. User-Adap. Inter. 26, 33–68 (2016)

    Article  Google Scholar 

  35. Mills, C., Bixler, R., Wang, X., D’Mello, S.K. Automatic gaze-based detection of mind wandering during film viewing. In: Proceedings of the International Conference on Educational Data Mining. International Educational Data Mining Society, pp. 30–37 (2016)

    Google Scholar 

Download references

Acknowledgments

This research was supported by the National Science Foundation (NSF) DRL 1235958 and IIS 1523091. Any opinions, findings and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF.

Author information

Authors and Affiliations

  1. University of New Hampshire, Durham, NH, USA

    Caitlin Mills

  2. University of Illinois at Urbana Champaign, Champaign, IL, USA

    Nigel Bosch

  3. University of Notre Dame, Notre Dame, IN, USA

    Kristina Krasich

  4. University of Colorado Boulder, Boulder, CO, USA

    Sidney K. D’Mello

Authors
  1. Caitlin Mills
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nigel Bosch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kristina Krasich
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sidney K. D’Mello
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Caitlin Mills .

Editor information

Editors and Affiliations

  1. University of Sao Paulo, Sao Paulo, Brazil

    Seiji Isotani

  2. University of Malaga, Málaga, Spain

    Eva Millán

  3. Carnegie Mellon University, Pittsburgh, PA, USA

    Amy Ogan

  4. DePaul University, Chicago, IL, USA

    Peter Hastings

  5. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  6. University College London, London, UK

    Rose Luckin

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mills, C., Bosch, N., Krasich, K., D’Mello, S.K. (2019). Reducing Mind-Wandering During Vicarious Learning from an Intelligent Tutoring System. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23204-7_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23203-0

  • Online ISBN: 978-3-030-23204-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation