Skip to main content

Effectiveness of System-Facilitated Monitoring Strategies on Learning in an Intelligent Tutoring System

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12784))

Abstract

To effectively process complex information within intelligent tutoring systems (ITSs), learners are required to engage in metacognitive monitoring micro-processes (content evaluations [CEs], judgments of learning [JOLs], feelings of knowing [FOKs], and monitoring progress towards goals [MPTGs]). Learners’ average monitoring micro-process strategy frequencies were used to examine learning gains using a person-centered approach as they interacted with MetaTutor. Undergraduates (n = 94) engaged in self-initiated and system-facilitated self-regulated learning (SRL) strategies as they studied the human circulatory system with MetaTutor, a hypermedia-based ITS. Using hierarchical clustering, results showed a difference in learning between clusters differing in metacognitive monitoring process usage. Specifically, learners who used both CEs and FOKs for a greater proportion of monitoring strategy usage had significantly greater learning gains than learners who used MPTGs. Implications for monitoring strategy usage across different micro-processes and the development of ITSs to facilitate and scaffold learners’ interactions with these micro-processes via prompting are discussed.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

References

  1. Winne, P.H., Azevedo, R.: Metacognition. In: Sawyer, K. (ed.) Handbook of the Learning Sciences. 3rd edn. Cambridge University Press, Cambridge, MA (in press)

    Google Scholar 

  2. Azevedo, R., Taub, M., Mudrick, N.V.: Understanding and reasoning about real-time cognitive, affective, and metacognitive processes to foster self-regulation with advanced learning technologies. In: Schunk, D.H., Greene, J.A. (eds.) Handbook of Self-regulation of Learning and Performance, 2nd edn., pp. 254–270. Routledge, New York (2018)

    Google Scholar 

  3. Josephsen, J.M.: A qualitative analysis of metacognition in simulation. J. Nursing Educ. 56, 675–678 (2017)

    Article  Google Scholar 

  4. Molenaar, I., Roda, C., van Boxtel, C., Sleegers, P.: Dynamic scaffolding of socially regulated learning in a computer-based learning environment. Comput. Educ. 59, 515–523 (2012)

    Article  Google Scholar 

  5. Schunk, D.H., Greene, J.A.: Handbook of Self-Regulation of Learning and Performance, 2nd edn. Routledge, New York (2018)

    Google Scholar 

  6. Panadero, E.: A review of self-regulated learning: six models and four directions for research. Front. Psychol. 8, 1–28 (2017)

    Article  Google Scholar 

  7. Zimmerman, B.J.: Attaining self-regulation: a social cognitive perspective. In: Boekaerts, M., Pintrich, P.R., Zeidner, M. (eds.) Handbook of Self-regulation, pp. 13–40. Academic Press, CA (2000)

    Chapter  Google Scholar 

  8. Pintrich, P.R.: The role of goal orientation in self-regulated learning. In: Boekaerts, M., Pintrich, P.R., Zeidner, M. (eds.) Handbook of Self-regulation, pp. 452–502. Academic Press, CA (2000)

    Google Scholar 

  9. Efklides, A.: Interactions of metacognition with motivation and affect in self-regulated learning: the MASRL model. Educ. Psychol. 46, 6–25 (2011)

    Article  Google Scholar 

  10. Winne, P.: Theorizing and researching levels of processing in self-regulated learning. Br. J. Educ. Psychol. 88, 9–20 (2018)

    Article  Google Scholar 

  11. McCardle, L., Hadwin, A.F.: Using multiple, contextualized data sources to measure learners’ perceptions of their self-regulated learning. Metacogn. Learn. 10, 43–75 (2015)

    Article  Google Scholar 

  12. Greene, J., Azevedo, R.: A micro-level analysis of SRL processes and their relations to the acquisition of a sophisticated mental model of a complex system. Contemp. Educ. Psychol. 34, 18–29 (2009)

    Article  Google Scholar 

  13. Azevedo, R.: Multimedia learning of metacognitive strategies. In: Mayer, R.E. (ed.) Handbook of Multimedia Learning, 2nd edn., pp. 647–672. Cambridge University Press (2014)

    Google Scholar 

  14. Paans, C., Molenaar, I., Segers, E., Verhoeven, L.: Temporal variation in children’s self-regulated hypermedia learning. Comput. Human Behav. 96, 246–258 (2019)

    Article  Google Scholar 

  15. Azevedo, R., Gašević, D.: Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies: issues and challenges. Comput. Human Behav. 96, 207–210 (2019)

    Article  Google Scholar 

  16. Johnson, W.L., Lester, J.C.: Face-to-face interaction with pedagogical agents, twenty years later. Int. J. Artif. Intell. Educ. 26, 25–36 (2016)

    Article  Google Scholar 

  17. D’Mello, S., Olney, A., Williams, C., Hays, P.: Gaze tutor: a gaze-reactive intelligent tutoring system. Int. J. Human-Comput. Stud. 70, 377–398 (2012)

    Article  Google Scholar 

  18. Bouchet, F., Harley, J., Azevedo, R.: Can adaptive pedagogical agents’ prompting strategies improve students’ learning and self-regulation? In: 13th International Conference on Intelligent Tutoring Systems, pp. 368–374 (2016)

    Google Scholar 

  19. Trevors, G., Duffy, M., Azevedo, R.: Note-taking within MetaTutor: interactions between an intelligent tutoring system and prior knowledge on note-taking and learning. Educ. Technol. Res. Dev. 62, 507–528 (2014)

    Article  Google Scholar 

  20. Greene, J.S., Bolick, C.M., Jackson, W.P., Caprino, A.M., Oswald, C., McVea, M.: Domain-specificity of self-regulated learning processing in science and history. Contemp. Educ. Psychol. 42, 111–128 (2015)

    Article  Google Scholar 

  21. Poitras, E., Lajoie, S.: Using technology-rich environments to foster self-regulated learning in the social studies. In: Schunk, D.H., Greene, J.A. (eds.) Handbook on Self-regulation of Learning and Performance, 2nd edn., pp. 254–270. Routledge, New York (2018)

    Google Scholar 

  22. Bouchet, F., Harley, J.M., Trevors, G.J., Azevedo, R.: Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. J. Educ. Data Min. 5, 104–146 (2013)

    Google Scholar 

  23. Marx, J., Cummings, K.: Normalized change. Am. J. Phys. 75, 87–91 (2007)

    Article  Google Scholar 

  24. R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing (2020)

    Google Scholar 

  25. Galili, T.: dendextend: an R package for visualizing, adjusting, and comparing trees of hierarchical clustering. Bioinformatics (2015)

    Google Scholar 

  26. Revelle, W.: psych: Procedures for Personality and Psychological Research, Northwestern University (2018)

    Google Scholar 

  27. Azevedo, R., Dever, D.: Multimedia learning and metacognitive strategies. In: Mayer, R.E., Fiorella, L. (eds.) The Cambridge Handbook of Multimedia Learning, 3rd edn. Cambridge University Press, New York (in press)

    Google Scholar 

  28. Graesser, A.C., Fiore, S.M., Greiff, S., Andrews-Todd, J., Foltz, P.W., Hesse, F.W.: Advancing the science of collaborative problem solving. Psychol. Sci. Public Interest 19, 59–92 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by funding from the National Science Foundation (DRL#1661202, DUE#1761178, DRL#1916417, IIS#1917728), the Social Sciences and Humanities Research Council of Canada (SSHRC 895-2011-1006). The authors would also like to thank members of the SMART Lab at UCF for their contributions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daryn A. Dever .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dever, D.A., Wortha, F., Wiedbusch, M.D., Azevedo, R. (2021). Effectiveness of System-Facilitated Monitoring Strategies on Learning in an Intelligent Tutoring System. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies: New Challenges and Learning Experiences. HCII 2021. Lecture Notes in Computer Science(), vol 12784. Springer, Cham. https://doi.org/10.1007/978-3-030-77889-7_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77889-7_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77888-0

  • Online ISBN: 978-3-030-77889-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics