Skip to main content
SpringerLink
Log in

Using Data Visualizations to Foster Emotion Regulation During Self-Regulated Learning with Advanced Learning Technologies

  • Chapter
  • First Online:
Informational Environments

Abstract

Emotions play a critical role during learning and problem solving with advanced learning technologies. However, learners typically do not accurately monitor and regulate their emotions and may therefore not learn as much, disengage from the task, and not optimize their learning of the instructional material. Despite their importance, relatively few attempts have been made to understand learners’ emotional monitoring and regulation during learning with advanced learning technologies by using data visualizations of their own (and others’) cognitive, affective, metacognitive, and motivational (CAMM) self-regulated learning (SRL) processes to potentially foster their emotion regulation during learning with advanced learning technologies. We present a theoretically-based and empirically-driven conceptual framework that addresses emotion regulation by proposing the use of visualizations of one’s and others’ CAMM-SRL multichannel data (e.g., cognitive strategy use, metacognitive monitoring accuracy, facial expressions of emotions, physiological arousal, eye-movement behaviors, etc.) to facilitate learners’ monitoring and regulation of their emotions during learning with advanced learning technologies. We use examples from several of our laboratory and classroom studies to illustrate a possible mapping between theoretical assumptions, emotion-regulation strategies, and the types of data visualizations that can be used to enhance and scaffold learners’ emotion regulation, including key processes such as emotion flexibility, emotion adaptivity, and emotion efficacy. We conclude with future directions that can lead to a systematic interdisciplinary research agenda that addresses outstanding emotion regulation-related issues by integrating models, theories, methods, and analytical techniques for the areas of cognitive, learning, and affective sciences, human computer interaction, data visualization, big data, data mining, data science, learning analytics, open learner models, and SRL.

An earlier version of this chapter was presented at the 7th International Learning Analytics and Knowledge (LAK 2017) conference, Vancouver, Canada and published as Azevedo, R., Millar, G. C., Taub, M., Mudrick, N. V., Bradbury, A. E., & Price, M. J. (2017). Using data visualizations to foster emotion regulation during self-regulated learning with advanced learning technologies: a conceptual framework. In X. Ochoa, I. Molenaar, & S. Dawson (Eds.), Proceedings of the 7th International Conference on Learning Analytics & Knowledge: Understanding, Informing, and Improving Learning (pp. 444–448). New York, NY: ACM.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    Up to this point in our chapter we have strategically not used the word scaffolding. We now emphasize scaffolding because there is an implicit assumption that learners will require scaffolding to accurately monitor and regulate their emotions.

References

  • Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2015). Emotion-regulation strategies across psychopathology: A meta-analytic review. Clinical Psychology Review, 30, 217–237.

    Article  Google Scholar 

  • Antonietti, A., Casellato, C., Garrido, J. A., Luque, N. R., Naveros, F., Ros, E., … Pedrocchi, A. (2015). Spiking neural network with distributed plasticity reproduces cerebellar learning in eye blink conditioning paradigms. IEEE Transactions on Biomedical Engineering, 63, 210–219.

    Article  PubMed  Google Scholar 

  • Azevedo, R. (2014a). Issues in dealing with sequential and temporal characteristics of self- and socially-regulated learning. Metacognition and Learning, 9, 217–228.

    Article  Google Scholar 

  • Azevedo, R. (2014b). Multimedia learning of metacognitive strategies. In R. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 647–672). Cambridge, MA: Cambridge University Press.

    Chapter  Google Scholar 

  • Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50, 84–94.

    Article  Google Scholar 

  • Azevedo, R., & Aleven, V. (2013a). Metacognition and learning technologies: An overview of the current interdisciplinary research. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 1–16). Amsterdam, The Netherlands: Springer.

    Chapter  Google Scholar 

  • Azevedo, R., & Aleven, V. (Eds.). (2013b). International handbook of metacognition and learning technologies. Amsterdam, The Netherlands: Springer.

    Google Scholar 

  • Azevedo, R., & Hadwin, A. F. (2005). Scaffolding self-regulated learning and metacognition—Implications for the design of computer-based scaffolds. Instructional Science, 33, 367–379.

    Article  Google Scholar 

  • Azevedo, R., Harley, J., Trevors, G., Feyzi-Behnagh, R., Duffy, M., Bouchet, F., & Landis, R. S. (2013). Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 427–449). Amsterdam, The Netherlands: Springer.

    Chapter  Google Scholar 

  • Azevedo, R., Martin, S. A., Taub, M., Mudrick, N., Millar, G., & Grafsgaard, J. (2016). Are pedagogical agents’ external regulation effective in fostering learning with intelligent tutoring systems? In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Proceedings of the 13th International Conference on Intelligent Tutoring Systems—Lecture Notes in Computer Science 9684 (pp. 197–207). Amsterdam, The Netherlands: Springer.

    Google Scholar 

  • Azevedo, R., Taub, M., & Mudrick, N. (2015). Technologies supporting self-regulated learning. In M. Spector, C. Kim, T. Johnson, W. Savenye, D. Ifenthaler, & G. Del Rio (Eds.), The SAGE Encyclopedia of educational technology (pp. 731–734). Thousand Oaks, CA: SAGE.

    Google Scholar 

  • Azevedo, R., Taub, M., Mudrick, N., Farnsworth, J., & Martin, S. A. (2016). Interdisciplinary research methods used to investigate emotions with advanced learning technologies. In M. Zembylas & P. Schutz (Eds.), Methodological advances in research on emotion and education (pp. 231–243). Amsterdam, The Netherlands: Springer.

    Google Scholar 

  • Barrett, L. F., Lewis, M., & Haviland-Jones, J. M. (Eds.). (2016). Handbook of emotions. New York, NY: The Guildford Press.

    Google Scholar 

  • Belland, B., Walker, A., Kim, N., & Lefler, M. (2017). Synthesizing results from empirical research on computer-based scaffolding in STEM education: A meta-analysis. Review of Educational Research, 87(2), 309–344.

    Article  PubMed  Google Scholar 

  • Biswas, G., Segedy, J. R., & Bunchongchit, K. (2016). From design to implementation to practice—A learning by teaching system: Betty’s brain. International Journal of Artificial Intelligence in Education, 26, 350–364.

    Article  Google Scholar 

  • Bonanno, G. A., & Burton, C. L. (2013). Regulatory flexibility an individual differences perspective on coping and emotion regulation. Perspectives on Psychological Science, 8, 591–612.

    Article  PubMed  Google Scholar 

  • Bosse, T., Gerritsen, C., Man, J. D., & Treur, J. (2013). Learning emotion regulation strategies: A cognitive agent model. In Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) (Vol. Vol 2, pp. 245–252). Washington, DC: IEEE Computer Society.

    Google Scholar 

  • Buder, J., & Hesse, F. W. (2017). Informational environments: Cognitive, motivational-affective, and social-affective forays into the digital transformation. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 1–25). New York: Springer.

    Google Scholar 

  • Bull, S., & Kay, J. (2016). SMILI: A framework for interfaces to learning data in open learner models, learning analytics and related fields. International Journal of Artificial Intelligence in Education, 26, 293–331.

    Article  Google Scholar 

  • Calvo, R. A., D’Mello, S., Gratch, J., & Kappas, A. (Eds.). (2015). The Oxford handbook of affective computing. Oxford, England: Oxford University Press.

    Google Scholar 

  • D’Mello, S. K. (2013). A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. Journal of Educational Psychology, 105, 1082–1099.

    Article  Google Scholar 

  • D’Mello, S., & Graesser, A. (2012). AutoTutor and affective AutoTutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems, 2, 1–39.

    Article  Google Scholar 

  • D’Mello, S. K., & Graesser, A. C. (2015). Feeling, thinking, and computing with affect-aware learning technologies. In R. A. Calvo, S. K. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford handbook of affective computing (pp. 419–434). New York, NY: Oxford University Press.

    Google Scholar 

  • D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170.

    Article  Google Scholar 

  • Doré, B. P., Silvers, J. A., & Ochsner, K. N. (2016). Toward a personalized science of emotion regulation. Social and Personality Psychology Compass, 10, 171–187.

    Article  Google Scholar 

  • Ekman, P. (1977). Facial expression. In A. Siegman, & S. Feldstein (Eds.), Nonverbal Communication and Behavior (pp. 97–126). New Jersey: Lawrence Erlbaum Association.

    Google Scholar 

  • Forbes-Riley, K., & Litman, D. (2011). Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor. Speech Communication, 53, 1115–1136.

    Article  Google Scholar 

  • Graesser, A. C. (2016). Conversations with AutoTutor help students learn. IJAIED, 26, 124–132.

    Google Scholar 

  • Graesser, A. C., Lippert, A., & Hampton, D. (2017). Successes and failures in building learning environments to promote deep learning: The value of conversational agents. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 273–298). New York, NY: Springer.

    Google Scholar 

  • Gratch, J., & Marsella, S. (2015). Appraisal models. In R. A. Calvo, S. K. D’Mello, J. Gratch, & A. Kappas (Eds.), Handbook of affective computing (pp. 57–67). Oxford, UK: Oxford University Press.

    Google Scholar 

  • Grawemeyer, B., Mavrikis, M., Holmes, W., Gutierrez-Santos, S., Wiedmann, M., & Rummel, N. (2016). Affecting off-task behaviour: How affect-aware feedback can improve student learning. In Proceedings of the 6th Int. Conference on Learning Analytics & Knowledge (pp. 104–113). New York, NY: Association for Computing Machinery (ACM).

    Chapter  Google Scholar 

  • Gross, J. J. (2015a). Emotion regulation: Current status and future prospects. Psychological Inquiry, 26, 1–26.

    Article  Google Scholar 

  • Gross, J. J. (2015b). The extended process model of emotion regulation: Elaborations, applications, and future prospects. Psychological Inquiry, 26, 1–26.

    Article  Google Scholar 

  • Harley, J. M., Bouchet, F., Hussain, S., Azevedo, R., & Calvo, R. (2015). A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 48, 615–625.

    Article  Google Scholar 

  • Harley, J. M., Poitras, E. G., Jarrell, A., Duffy, M. C., & Lajoie, S. P. (2016). Comparing virtual and location-based augmented reality mobile learning: emotions and learning outcomes. Educational Technology Research and Development, 64(3), 359–388.

    Article  Google Scholar 

  • Kappas, A. (2013). Social regulation of emotion: Messy layers. Frontiers in psychology, 4, 1–11.

    Article  Google Scholar 

  • Lafford, B. A. (2004). Review of tell me more Spanish. Journal on Language Learning and Technology, 8, 21–34.

    Google Scholar 

  • Litman, D., & Forbes-Riley, K. (2014). Evaluating a spoken dialogue system that detects and adapts to user affective states. In Proceedings of the 15th Meeting of the Special Interest Group on Discourse and Dialogue (pp. 181–185).

    Chapter  Google Scholar 

  • Mayer, R. (2014). Multimedia instruction. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4th ed., pp. 385–400). Amsterdam, The Netherlands: Springer.

    Chapter  Google Scholar 

  • Muldner, K., Wixon, M., Rai, D., Burleson, W., Woolf, B., & Arroyo, I. (2015). Exploring the impact of a learning dashboard on student affect. In C. Conati, N. Heffernan, A. Mitrovic, & M. Verdejo (Eds.), Artificial intelligence in education (pp. 307–317). Heidelberg, Germany: Springer.

    Chapter  Google Scholar 

  • Nye, B. D., Graesser, A. C., & Hu, X. (2014). Multimedia learning with intelligent tutoring systems. In R. Mayer (Ed.), Cambridge handbook of multimedia learning (3rd ed., pp. 705–728). Cambridge, MA: Cambridge University Press.

    Chapter  Google Scholar 

  • Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: Using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24, 27–45.

    Article  Google Scholar 

  • Pekrun, R., & Linnenbrink-Garcia, L. (Eds.). (2014). International handbook of emotions in education. New York, NY: Routledge.

    Google Scholar 

  • Porayska-Pomsta, K., Mavrikis, M., D’Mello, S., Conati, C., & Baker, R. S. (2013). Knowledge elicitation methods for affect modelling in education. International Journal of Artificial Intelligence in Education, 22, 107–140.

    Google Scholar 

  • Reeck, C., Ames, D. R., & Ochsner, K. N. (2016). The social regulation of emotion: An integrative, cross-disciplinary model. Trends in Cognitive Sciences, 20, 47–63.

    Article  PubMed  Google Scholar 

  • Sabourin, J., & Lester, J. (2014). Affect and engagement in game-based learning environments. IEE Transactions on Affective Computing, 5, 45–56.

    Article  Google Scholar 

  • Santos, J. L., Govaerts, S., Verbert, K., & Duval, E. (2012). Goal-oriented visualizations of activity tool tracking: A case study with engineering students. In S. Buckingham Shum, D. Gašević, & R. Ferguson (Eds.), Proceedings of the 2nd International Conference on Learning Analytics and Knowledge LAK ′12 (pp. 143–152). New York, NY: ACM.

    Chapter  Google Scholar 

  • Scheiter, K., Fillisch, B., Krebs, M., Leber, J., Ploetzner, R., Renkl, A., … Zimmermann, G. (2017). How to design adaptive multimedia environments to support self-regulated learning. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 203–233). New York, NY: Springer.

    Google Scholar 

  • Scherer, K. R. (2009). The dynamic architecture of emotion: Evidence for the component process model. Cognition and Emotions, 7, 1307–1351.

    Article  Google Scholar 

  • Schunk, D. H., & Greene, J. A. (Eds.). (in press). Handbook of self-regulation of learning and performance (2nd ed.). New York, NY: Routledge.

    Google Scholar 

  • Suri, G., & Gross, J. J. (2016). Emotion regulation: A valuation perspective. In L. F. Barrett, M. Lewis, & J. M. Haviland-Jones (Eds.), Handbook of emotions (4th ed.). New York, NY: The Guildford Press.

    Google Scholar 

  • Taub, M., Mudrick, N., Azevedo, R., Millar, G., Rowe, J., & Lester, J. (2016). Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with Crystal Island. In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Proceedings of the 13th ITS Conference. Lecture Notes in Computer Science 9684 (pp. 240–246). The Netherlands: Springer.

    Google Scholar 

  • Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., & Lester, J. (2017). Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with CRYSTAL ISLAND. Computers in Human Behavior.

    Google Scholar 

  • Van Kleef, G. A. (2009). How emotions regulate social life the emotions as social information (EASI) model. Current Directions in Psychological Science, 18, 184–188.

    Article  Google Scholar 

  • van Kleef, G. A. (2016). Toward an integrative theory of emotions as social information. Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57, 1500–1509.

    Article  Google Scholar 

  • Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Assche, F., Parra, G., & Klerkx, J. (2013). Learning dashboards: An overview and future research opportunities. Personal and Ubiquitous Computing, 18, 1499–1514.

    Google Scholar 

  • Winne, P. H., & Azevedo, R. (2014). Metacognition. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 63–87). Cambridge, England: Cambridge University Press.

    Chapter  Google Scholar 

  • Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Winne, P. H., Vytaske, J., Patzak, A., Rakovic, M., Marzouk, A., Pakdaman-Sajovi, A., … Nesbit, J. (2017). Designs for learning analytics to support information problem solving. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 249–272). New York, NY: Springer.

    Google Scholar 

  • Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009). Affect-aware tutors: Recognizing and responding to student affect. International Journal of Learning Technology, 4, 129–164.

    Article  Google Scholar 

  • Zimmerman, B. J., & Schunk, D. H. (Eds.). (2011). Handbook of self-regulation of learning and performance. New York, NY: Routledge.

    Google Scholar 

Download references

Acknowledgements

This chapter was supported by funding from the National Science Foundation (DRL#1431552, DRL#1660878, CMMI#1744351) and the Social Sciences and Humanities Research Council of Canada (SSHRC 895-2011-1006). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or Social Sciences and Humanities Research Council of Canada.

The authors would like to thank Carina Tudela, Mitchell Moravec, Alex Haikonen, and Pooja Ganatra from the SMART Lab at NCSU for their assistance.

Author information

Authors and Affiliations

  1. North Carolina State University, 2310 Stinson Drive, 640 Poe Hall, Raleigh, NC, 27695, USA

    Roger Azevedo, Michelle Taub, Nicholas V. Mudrick, Garrett C. Millar, Amanda E. Bradbury & Megan J. Price

Authors
  1. Roger Azevedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michelle Taub
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicholas V. Mudrick
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Garrett C. Millar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amanda E. Bradbury
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Megan J. Price
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roger Azevedo .

Editor information

Editors and Affiliations

  1. Leibniz-Institut für Wissensmedien (IWM), Tübingen, Germany

    Jürgen Buder

  2. Leibniz-Institut für Wissensmedien (IWM), Tübingen, Germany

    Friedrich W. Hesse

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Azevedo, R., Taub, M., Mudrick, N.V., Millar, G.C., Bradbury, A.E., Price, M.J. (2017). Using Data Visualizations to Foster Emotion Regulation During Self-Regulated Learning with Advanced Learning Technologies. In: Buder, J., Hesse, F. (eds) Informational Environments . Springer, Cham. https://doi.org/10.1007/978-3-319-64274-1_10

Download citation

Publish with us

Policies and ethics

Search

Navigation