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A Review of Measurements and Techniques to Study Emotion Dynamics in Learning

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Unobtrusive Observations of Learning in Digital Environments

Part of the book series: Advances in Analytics for Learning and Teaching ((AALT))

Abstract

Emotion states are dynamic and contextual across learning environments. Learners who experience similar levels of emotions can differ substantially in the fluctuation of emotions in a task or throughout a course. However, research on emotion dynamics is still limited and fragmented in teaching and learning contexts. Despite an increasing interest from researchers to investigate the dynamic aspect of students’ emotions, there has been no review of measurements and techniques to study emotion dynamics. We address this gap by introducing a taxonomy of emotion dynamics features, i.e., emotional variability, emotional instability, emotional inertia, emotional cross-lags, and emotional patterns. Furthermore, we synthesize the current emotion detection methods that can unobtrusively capture longitudinal and time series data of emotions. These methods include experience sampling methods, emote-aloud, facial expressions, vocal expressions, language and discourse, and physiological sensors. Moreover, this review introduces the predominant analytical techniques that can quantify emotion dynamics from longitudinal and time series data. We demonstrate how the conventional statistical methods have been used to quantify different features of emotion dynamics. We also present some emerging techniques for assessing emotion dynamics, including entropy analysis, growth curve modeling, time series analysis, network analysis, recurrence quantification analysis, and sequential pattern mining. The emotion detection and analytical approaches described in this chapter provide researchers a practical guide in examining emotion dynamics in teaching and learning contexts. This chapter also has theoretical importance since it will help researchers develop a dynamic perspective of emotions and will promote a deep understanding of emotion generation and regulation.

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References

  • Ahmed, W., van der Werf, G., Kuyper, H., & Minnaert, A. (2013). Emotions, self-regulated learning, and achievement in mathematics: A growth curve analysis. Journal of Educational Psychology, 105(1), 150–161. https://doi.org/10.1037/a0030160

    Article  Google Scholar 

  • Bachorowski, J.-A., & Owren, M. J. (1995). Vocal expression of emotion: Acoustic properties of speech are associated with emotional intensity and context. Psychological Science, 6(4), 219–224.

    Article  Google Scholar 

  • Bailen, N. H., Green, L. M., & Thompson, R. J. (2019). Understanding emotion in adolescents: A review of emotional frequency, intensity, instability, and clarity. Emotion Review, 11(1), 63–73.

    Article  Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

    Google Scholar 

  • Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., Borsboom, D., & Tuerlinckx, F. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLoS One, 8(4), e60188.

    Article  Google Scholar 

  • Bringmann, L. F., Pe, M. L., Vissers, N., Ceulemans, E., Borsboom, D., Vanpaemel, W., Tuerlinckx, F., & Kuppens, P. (2016). Assessing temporal emotion dynamics using networks. Assessment, 23(4), 425–435.

    Article  Google Scholar 

  • Bringmann, L. F., Hamaker, E. L., Vigo, D. E., Aubert, A., Borsboom, D., & Tuerlinckx, F. (2017). Changing dynamics: Time-varying autoregressive models using generalized additive modeling. Psychological Methods, 22(3), 409.

    Article  Google Scholar 

  • Carstensen, L. L., Pasupathi, M., Mayr, U., & Nesselroade, J. R. (2000). Emotional experience in everyday life across the adult life span. Journal of Personality and Social Psychology, 79(4), 644.

    Article  Google Scholar 

  • Cincotta, P. M., Giordano, C. M., Silva, R. A., & BeaugĂ©, C. (2021). The Shannon entropy: An efficient indicator of dynamical stability. Physica D: Nonlinear Phenomena, 417, 132816.

    Article  Google Scholar 

  • Craig, S. D., D’Mello, S., Witherspoon, A., & Graesser, A. (2008). Emote aloud during learning with AutoTutor: Applying the facial action coding system to cognitive–affective states during learning. Cognition and Emotion, 22(5), 777–788.

    Article  Google Scholar 

  • Curran, P. J., Obeidat, K., & Losardo, D. (2010). Twelve frequently asked questions about growth curve modeling. Journal of Cognition and Development, 11(2), 121–136. https://doi.org/10.1080/15248371003699969

    Article  Google Scholar 

  • D’Mello, S. K., Craig, S. D., Sullins, J., & Graesser, A. C. (2006). Predicting affective states expressed through an emote-aloud procedure from AutoTutor’s mixed-initiative dialogue. International Journal of Artificial Intelligence in Education, 16(1), 3–28.

    Google Scholar 

  • Ekman, P. (1993). Facial expression and emotion. American Psychologist, 48(4), 384.

    Article  Google Scholar 

  • Ekman, P., & Friesen, W. V. (1976). Measuring facial movement. Environmental Psychology and Nonverbal Behavior, 1(1), 56–75.

    Article  Google Scholar 

  • Fleuchaus, E., Kloos, H., Kiefer, A. W., & Silva, P. L. (2020). Complexity in science learning: Measuring the underlying dynamics of persistent mistakes. Journal of Experimental Education, 88(3), 448–469. https://doi.org/10.1080/00220973.2019.1660603

    Article  Google Scholar 

  • Gross, J. J. (2013). Emotion regulation: Conceptual and empirical foundations. In J. J. Gross (Ed.), Handbook of emotion regulation (2nd ed., pp. 3–20). Guilford Publications.

    Google Scholar 

  • Harley, J. M. (2016). Measuring emotions: A survey of cutting edge methodologies used in computer-based learning environment research. In S. Y. Tettegah & M. Gartmeier (Eds.), Emotions, technology, design, and learning (pp. 89–114). Academic Press. https://doi.org/10.1016/B978-0-12-801856-9.00005-0

    Chapter  Google Scholar 

  • Hilpert, J. C., & Marchand, G. C. (2018). Complex systems research in educational psychology: Aligning theory and method. Educational Psychologist, 53(3), 185–202. https://doi.org/10.1080/00461520.2018.1469411

    Article  Google Scholar 

  • Houben, M., Van Den Noortgate, W., & Kuppens, P. (2015). The relation between short-term emotion dynamics and psychological Well-being: A meta-analysis. Psychological Bulletin, 141(4), 901.

    Article  Google Scholar 

  • Jack, R. E., Garrod, O. G. B., & Schyns, P. G. (2014). Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time. Current Biology, 24(2), 187–192.

    Article  Google Scholar 

  • Jenkins, B. N., Hunter, J. F., Richardson, M. J., Conner, T. S., & Pressman, S. D. (2020). Affect variability and predictability: Using recurrence quantification analysis to better understand how the dynamics of affect relate to health. Emotion, 20(3), 391–402. https://doi.org/10.1037/emo0000556

    Article  Google Scholar 

  • Kashdan, T. B., & Rottenberg, J. (2010). Psychological flexibility as a fundamental aspect of health. Clinical Psychology Review, 30(7), 865–878.

    Article  Google Scholar 

  • Kim, K. H., Bang, S. W., & Kim, S. R. (2004). Emotion recognition system using short-term monitoring of physiological signals. Medical and Biological Engineering and Computing, 42(3), 419–427.

    Article  Google Scholar 

  • Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2011). Deap: A database for emotion analysis using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18–31.

    Article  Google Scholar 

  • Krone, T., Albers, C. J., Kuppens, P., & Timmerman, M. E. (2017). A multivariate statistical model for emotion dynamics. Emotion, 18(5), 739–754. https://doi.org/10.1037/emo0000384

    Article  Google Scholar 

  • Kuchibhotla, S., Vankayalapati, H. D., Vaddi, R. S., & Anne, K. R. (2014). A comparative analysis of classifiers in emotion recognition through acoustic features. International Journal of Speech Technology, 17(4), 401–408.

    Article  Google Scholar 

  • Kuppens, P., & Verduyn, P. (2015). Looking at emotion regulation through the window of emotion dynamics. Psychological Inquiry, 26(1), 72–79.

    Article  Google Scholar 

  • Kuppens, P., & Verduyn, P. (2017). Emotion dynamics. Current Opinion in Psychology, 17, 22–26. https://doi.org/10.1016/j.copsyc.2017.06.004

    Article  Google Scholar 

  • Kuppens, P., Stouten, J., & Mesquita, B. (2009). Individual differences in emotion components and dynamics: Introduction to the special issue. Cognition and Emotion, 23(7), 1249–1258.

    Article  Google Scholar 

  • Kuppens, P., Allen, N. B., & Sheeber, L. B. (2010). Emotional inertia and psychological maladjustment. Psychological Science, 21(7), 984–991.

    Article  Google Scholar 

  • Lajoie, S. P., Zheng, J., Li, S., Jarrell, A., & Gube, M. (2019). Examining the interplay of affect and self regulation in the context of clinical reasoning. Learning and Instruction, 101219, 101219. https://doi.org/10.1016/j.learninstruc.2019.101219

    Article  Google Scholar 

  • Li, S., Zheng, J., & Lajoie, S. P. (2021a). The frequency of emotions and emotion variability in self-regulated learning: What matters to task performance ? Frontline Learning Research, 9(4), 76–91.

    Article  Google Scholar 

  • Li, S., Zheng, J., Lajoie, S. P., & Wiseman, J. (2021b). Examining the relationship between emotion variability, self-regulated learning, and task performance in an intelligent tutoring system. Educational Technology Research and Development, 1–20. https://doi.org/10.1007/s11423-021-09980-9

  • Li, S., Zheng, J., Huang, X., & Xie, C. (2022). Self-regulated learning as a complex dynamical system: Examining students’ STEM learning in a simulation environment. Learning and Individual Differences, 95, 102144. https://doi.org/10.1016/j.lindif.2022.102144

    Article  Google Scholar 

  • Marwaha, S., He, Z., Broome, M., Singh, S. P., Scott, J., Eyden, J., & Wolke, D. (2014). How is affective instability defined and measured? A systematic review. Psychological Medicine, 44(9), 1793–1808.

    Article  Google Scholar 

  • Muis, K. R., Etoubashi, N., & Denton, C. A. (2020). The catcher in the lie: The role of emotions and epistemic judgments in changing students’ misconceptions and attitudes in a post-truth era. Contemporary Educational Psychology, 62, 101898.

    Article  Google Scholar 

  • Napa Scollon, C., Prieto, C.-K., & Diener, E. (2009). Experience sampling: Promises and pitfalls, strength and weaknesses. In E. Diener (Ed.), Assessing Well-being: The collected works of Ed Diener (pp. 157–180). Springer.

    Google Scholar 

  • Oliver, M. N. I., & Simons, J. S. (2004). The affective lability scales: Development of a short-form measure. Personality and Individual Differences, 37, 1279–1288. https://doi.org/10.1016/j.paid.2003.12.013

    Article  Google Scholar 

  • Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9

    Article  Google Scholar 

  • Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91–105.

    Article  Google Scholar 

  • Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. University of Texas at Austin.

    Google Scholar 

  • Rajaram, R., Castellani, B., & Wilson, A. N. (2017). Advancing Shannon entropy for measuring diversity in systems. Complexity, 8715605, 1. https://doi.org/10.1155/2017/8715605

    Article  Google Scholar 

  • Reitsema, A. M., Jeronimus, B. F., van Dijk, M., & de Jonge, P. (2022). Emotion dynamics in children and adolescents: A meta-analytic and descriptive review. Emotion, 22(2), 374–396. https://doi.org/10.1037/emo0000970

    Article  Google Scholar 

  • Röcke, C., Li, S.-C., & Smith, J. (2009). Intraindividual variability in positive and negative affect over 45 days: Do older adults fluctuate less than young adults? Psychology and Aging, 24(4), 863.

    Article  Google Scholar 

  • Scherer, K. R., Johnstone, T., & Klasmeyer, G. (2003). Vocal expression of emotion. In R. J. Davidson, K. R. Scherer, & H. H. Goldsmith (Eds.), Handbook of affective sciences (pp. 433–456). Oxford University Press.

    Google Scholar 

  • Schutz, P. A., & Davis, H. A. (2000). Emotions and self-regulation during test taking. Educational Psychologist, 35(4), 243–256. https://doi.org/10.1207/S15326985EP3504

    Article  Google Scholar 

  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423.

    Article  Google Scholar 

  • Smidt, K. E., & Suvak, M. K. (2015). A brief, but nuanced, review of emotional granularity and emotion differentiation research. Current Opinion in Psychology, 3, 48–51.

    Article  Google Scholar 

  • Sperry, S. H., Walsh, M. A., & Kwapil, T. R. (2020). Emotion dynamics concurrently and prospectively predict mood psychopathology. Journal of Affective Disorders, 261, 67–75.

    Article  Google Scholar 

  • Sun, J., Schwartz, H. A., Son, Y., Kern, M. L., & Vazire, S. (2020). The language of Well-being: Tracking fluctuations in emotion experience through everyday speech. Journal of Personality and Social Psychology, 118(2), 364.

    Article  Google Scholar 

  • Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54. https://doi.org/10.1177/0261927X09351676

    Article  Google Scholar 

  • Wallot, S. (2017). Recurrence quantification analysis of processes and products of discourse: A tutorial in R. Discourse Processes, 54(5–6), 382–405.

    Article  Google Scholar 

  • Xing, W., Tang, H., & Pei, B. (2019). Beyond positive and negative emotions: Looking into the role of achievement emotions in discussion forums of MOOCs. The Internet and Higher Education, 43, 100690.

    Article  Google Scholar 

  • Zheng, J., Huang, L., Li, S., Lajoie, S. P., Chen, Y., & Hmelo-Silver, C. E. (2021). Self-regulation and emotion matter: A case study of instructor interactions with a learning analytics dashboard. Computers & Education, 161, 104061.

    Article  Google Scholar 

  • Zirkel, S., Garcia, J. A., & Murphy, M. C. (2015). Experience-sampling research methods and their potential for education research. Educational Researcher, 44(1), 7–16. https://doi.org/10.3102/0013189X14566879

    Article  Google Scholar 

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Authors and Affiliations

  1. Lehigh University, Bethlehem, PA, USA

    Juan Zheng & Shan Li

  2. McGill University, Montreal, QC, Canada

    Susanne P. Lajoie

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  1. Juan Zheng
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  2. Shan Li
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  3. Susanne P. Lajoie
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Correspondence to Juan Zheng .

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Editors and Affiliations

  1. Centre for Change and Complexity, University of South Australia, Adelaide, SA, Australia

    Vitomir Kovanovic

  2. School of Modeling Simulation and Training, University of Central Florida, Orlando, FL, USA

    Roger Azevedo

  3. Data Science in Higher Education Learning and Teaching, Curtin University, Bentley, WA, Australia

    David C. Gibson

  4. Learning, Design and Technology, University of Mannheim, Mannheim, BW, Germany

    Dirk lfenthaler

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Zheng, J., Li, S., Lajoie, S.P. (2023). A Review of Measurements and Techniques to Study Emotion Dynamics in Learning. In: Kovanovic, V., Azevedo, R., Gibson, D.C., lfenthaler, D. (eds) Unobtrusive Observations of Learning in Digital Environments. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-031-30992-2_2

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  • DOI: https://doi.org/10.1007/978-3-031-30992-2_2

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