Skip to main content

Utilizing Dynamic Bayes Nets to Improve Early Prediction Models of Self-regulated Learning

  • Conference paper
User Modeling, Adaptation, and Personalization (UMAP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7899))

Abstract

Student engagement and motivation during learning activities is tied to better learning behaviors and outcomes and has prompted the development of learner-guided environments. These systems attempt to personalize learning by allowing students to select their own tasks and activities. However, recent evidence suggests that not all students are equally capable of guiding their own learning. Some students are highly self-regulated learners and are able to select learning goals, identify appropriate tasks and activities to achieve these goals and monitor their progress resulting in improved learning and motivational benefits over traditional learning tasks. Students who lack these skills are markedly less successful in self-guided learning environments and require additional scaffolding to be able to navigate them successfully. Prior work has examined these phenomena within the learner-guided environment, Crystal Island, and identified the need for early prediction of students’ self-regulated learning abilities. This work builds upon these findings and presents a dynamic Bayesian approach that significantly improves the classification accuracy of student self-regulated learning skills.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Baker, R.S., D’Mello, S., Rodrigo, S.K., Graesser, A.C.: 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, 223–241 (2010)

    Article  Google Scholar 

  2. Kanfer, R., Ackerman, P.L.: Motivation and Cognitive Abilities: An Integrative/Aptitude-Treatment Interaction Approach to Skill Acquisition. Journal of Applied Psychology 74, 657–690 (1989)

    Article  Google Scholar 

  3. Pekrun, R., Goetz, T., Titz, W., Perry, R.: Academic Emotions in Students’ Self-Regulated Learning and Achievement: A Program of Qualitative and Quantitative Research. Educational Psychologist 37, 91–105 (2002)

    Article  Google Scholar 

  4. Picard, R.W., et al.: Affective Learning — A Manifesto. BT Technology Journal 22, 253–269 (2004)

    Article  Google Scholar 

  5. Young, J.D.: The Effect of Self-Regulated Learning Strategies on Performance in Learner Controlled Computer-Based Instruction. Educational Technology Research and Development 144, 17–27 (1996)

    Article  Google Scholar 

  6. Easterday, M.W., Aleven, V., Scheines, R., Carver, S.M.: Using tutors to improve educational games. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS, vol. 6738, pp. 63–71. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  7. Pintrich, P.R., Groot, E.D.: Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology 82, 33–40 (1990)

    Article  Google Scholar 

  8. Pintrich, P.R.: A Conceptual Framework for Assessing Motivation and Self-Regulated Learning in College Students. Educational Psychology Review 16, 385–407 (2004)

    Article  Google Scholar 

  9. Kirschner, P.A., Sweller, J., Clark, R.E.: Why Minimal Guidance during instruction does not work: An analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching. Educational Psychologist 41, 75–86 (2006)

    Article  Google Scholar 

  10. Alfieri, L., Brooks, P., Aldrich, N., Tenenbaum, H.: Does Discovery-Based Instruction Enhance Learning. Journal of Education Psychology 103, 1–18 (2011)

    Article  Google Scholar 

  11. Ellis, D., Zimmerman, B.J.: Enhancing self-monitoring during self-regulated learning of speech, pp. 205–228 (2001)

    Google Scholar 

  12. Azevedo, R., Moos, D.C., Greene, J.A., Winters, F.I., Cromley, J.G.: Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Educational Technology Research and Development 56, 45–72 (2008)

    Article  Google Scholar 

  13. Zimmerman, B.J.: Self-regulated learning and academic achievement: An overview. Educational Psychologist 25, 3–17 (1990)

    Article  Google Scholar 

  14. Kostons, D., van Gog, T., Paas, F.: Training Self-Assessment and Task-Selection Skills: A Cognitive Approach to Improving Self-Regulated Learning. Learning and Instruction 22, 121–132 (2012)

    Article  Google Scholar 

  15. Azevedo, R., Cromley, J.G., Winters, F.I., Moos, D.C., Greene, J.A.: Adaptive human scaffolding facilitates adolescents’ self-regulated learning with hypermedia. Instructional Science 33, 381–412 (2005)

    Article  Google Scholar 

  16. 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, 101–128 (2006)

    Google Scholar 

  17. Fiorella, L., Mayer, R.E.: Paper-based aids for learning with a computer-based game. Journal of Educational Psychology 104, 1074–1082 (2012)

    Article  Google Scholar 

  18. Ifenthaler, D.: Determining the Effectiveness of Prompts for Self-Regulated Learning in Problem-Solving Scenarios. Educational Technology & Society 15, 38–52 (2012)

    Google Scholar 

  19. Kauffman, D.: Self-Regulated Learning in Web-Based Environments: Instructional Tools Designed to Facilitate Cognitive Strategy Use, Metacognitive Processing, and Motivational Beliefs. Journal of Educational Computing Research 30, 139–161 (2004)

    Article  Google Scholar 

  20. White, B., Frederiksen, J.: Inquiry, Modeling, and Metacognition: Making Science Accessible to All Students. Cognition & Instruction 16, 3–118 (1998)

    Article  Google Scholar 

  21. Davis, E.: Prompting Middle School Science Students for Productive Reflection: Generic and Directed Prompts. Journal of the Learning Sciences 12, 91–142 (2003)

    Article  Google Scholar 

  22. Koedinger, K.R., Aleven, V.: Exploring the Assistance Dilemma in Experiments with Cognitive Tutors. Educational Psychology Review 19, 239–364 (2007)

    Article  Google Scholar 

  23. Sabourin, J., Shores, L.R., Mott, B.W., Lester, J.C.: Predicting student self-regulation strategies in game-based learning environments. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 141–150. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  24. Azevedo, R., et al.: The effectiveness of pedagogical agents’ prompting and feedback in facilitating co-adapted learning with metaTutor. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 212–221. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  25. Azevedo, R., Johnson, A., Chauncey, A., Burkett, C.: Self-Regulated Learning with MetaTutor: Advancing the Science of Learning with MetaCognitive Tools. In: New Science of Learning: Cognition, Computers and Collaboration in Education, pp. 225–248 (2010)

    Google Scholar 

  26. Biswas, G., Jeong, H., Roscoe, R.: Promoting Motivation and Self-Regulated Learning Skills through Social Interactions in Agent-Based Learning Environments. In: 2009 AAAI Fall Symposium on Cognitive and Metacognitive Educational Systems (2009)

    Google Scholar 

  27. Kinnebrew, J.S., Biswas, G.: Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution. In: Proceedings of the 5th International Conference on Educational Data Mining (2012)

    Google Scholar 

  28. Aleven, V., Roll, I., McLaren, B.M., Koedinger, K.R.: Automated, Unobtrusive, Action-by-Action Assessment of Self-Regulation During Learning with an Intelligent Tutoring System. Educational Psychologist 45, 224–233 (2010)

    Article  Google Scholar 

  29. Shores, L.R., Rowe, J.P., Lester, J.C.: Early prediction of cognitive tool use in narrative-centered learning environments. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS, vol. 6738, pp. 320–327. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  30. Land, S.: Cognitive requirements for learning with open-ended learning environments. Educational Technology Research and Development 48, 61–78 (2000)

    Article  Google Scholar 

  31. Zimmerman, B.: Goal Setting: A Key Proactive Source of Academic Self-Regulation. Motivation and Self-Regulated Learning: Theory, Research, and Applications, 267–286 (2008).

    Google Scholar 

  32. Rowe, J.P., Shores, L.R., Mott, B.W., Lester, J.C.: Integrating Learning, Problem Solving, and Engagement in Narrative-Centered Learning Environments. International Journal of Artificial Intelligence in Education, 166–177 (2011)

    Google Scholar 

  33. McCrae, R., Costa, P.: Personality in Adulthood: A Five-Factor Theory Perspective. Guilford Press, New York (1993)

    Google Scholar 

  34. Elliot, A.J., McGregor, H.A.: A 2 x 2 achievement goal framework. Journal of Personality and Social Psychology 80, 501–519 (2001)

    Article  Google Scholar 

  35. Gernefski, N., Kraati, V.: Cognitive Emotion Regulation Questionnaire: Development of a Short 18-Item Version. Personality and Individual Differences 41, 1045–1053 (2006)

    Article  Google Scholar 

  36. McAuley, E., Duncan, T., Tammen, V.: Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: A confirmatory factory analysis. Research Quarterly for Exercise and Sport 60, 48–58 (1989)

    Article  Google Scholar 

  37. Witmer, B.G., Singer, M.J.: Measuring Presence in Virtual Environments: A Presence Questionnaire. Presence: Teleoperators and Virtual Environments 7, 225–240 (1998)

    Article  Google Scholar 

  38. Sabourin, J., Rowe, J., Mott, B., Lester, J.C.: When Off-Task is On-Task: The Affective Role of Off-Task Behavior in Narrative-Centered Learning Environments. In: Proceedings of the 15th International Conference on Artificial Intelligence and Education, pp. 534–536 (2011)

    Google Scholar 

  39. Baker, R.S.J.d., Corbett, A.T., Aleven, V.: More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 406–415. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  40. Corbett, A.T., Anderson, J.R.: Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction 4, 253–278 (1994)

    Article  Google Scholar 

  41. Conati, C., Maclaren, H.: Empirically building and evaluating a probabilistic model of user affect. User Modeling and User-Adapted Interaction 19, 267–303 (2009)

    Article  Google Scholar 

  42. Sabourin, J.L., Mott, B.W., Lester, J.C.: Modeling Learner Affect with Theoretically Grounded Dynamic Bayesian Networks. In: Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction, pp. 286–295 (2011)

    Google Scholar 

  43. Gertner, A., Conati, C., VanLehn, K.: Procedural help in Andes: Generating hints using a Bayesian network student model. In: Proceedings of the 15th National Conference on Artificial Intelligence (1998)

    Google Scholar 

  44. Alpaydin, E.: Introduction to Machine Learning. MIT Press (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sabourin, J., Mott, B., Lester, J. (2013). Utilizing Dynamic Bayes Nets to Improve Early Prediction Models of Self-regulated Learning. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38844-6_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38843-9

  • Online ISBN: 978-3-642-38844-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics