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Measuring Multidimensional Facets of SRL Engagement with Multimodal Data

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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

Essential to achieving adaptive intelligent AI-based education systems is theoretically grounded data measurement and analysis, and the subsequent data-supported individualized interventions that foster learner-system engagement. However, engagement is a challenging psychological construct to define and measure given the variation of theoretical conceptualizations of engagement and the various facets of engagements (e.g., behavioral, emotional, agentic, (meta)cognitive, and self-regulated learning). In this chapter we (1) define and situate a multifaceted conceptualization of engagement (based on the interrelated aspects of student engagement) within SRL, (2) introduce the integrative model of multidimensional self-regulated learning engagement to include cognitive, emotional, and behavioral facets of engagement; (3) briefly review the current conceptual, theoretical, and methodological approaches to measuring engagement and showcase how the use of multimodal data for this work has contributed to our understanding of learning in learning systems. Engagement-relevant data discussed within this chapter includes self-reports, log or behavioral streams, oculometrics, physiological sensors (e.g., skin conductance, heart-rate, etc.), facial expressions, body gestures, and think- and emote-alouds. We can leverage these multimodal data to reflect the dynamic and nonlinear nature of engagement that are frequently obfuscated by traditional unimodal methods (e.g., self-reports). However, it is crucial that when multimodal data is converged for this purpose, we consider a unifying theoretical grounding of engagement that is general enough to be applied across intelligent systems and the contexts in which they are used but specific enough to be useful in the design and development of analytical methods.; and (4) provide a methodological overview with contextualized examples to inform the research study design of future testing and validation of our integrative model of multidimensional self-regulated learning engagement using multimodal data. Our methodological overview identifies how different modalities of measurement and their temporal granularity contribute to the measurement of engagement as it fluctuates within the different phases of self-regulated learning. We conclude our chapter with an exploration of the implications of this guide as well as future directions for researchers, instructional designers, and software engineers capturing and analyzing engagement in digital environments using multimodal data.

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Acknowledgments

We wish to thank all of the current and past members of the UCF SMART Lab for their support with our ongoing research. The contributions of Roger Azevedo have been supported by several grants from the National Science Foundation (DRL#1661202, DRL#1916417, IIS#1917728, and BCS#2128684).

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Authors and Affiliations

  1. University of Central Florida, Orlando, FL, USA

    Megan Wiedbusch, Daryn Dever, Mary Jean Amon & Roger Azevedo

  2. Lehigh University, Bethlehem, PA, USA

    Shan Li

  3. McGill University, Montreal, QC, Canada

    Susanne Lajoie

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  1. Megan Wiedbusch
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  2. Daryn Dever
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  3. Shan Li
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  4. Mary Jean Amon
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  5. Susanne Lajoie
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  6. Roger Azevedo
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Correspondence to Megan Wiedbusch .

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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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Wiedbusch, M., Dever, D., Li, S., Amon, M.J., Lajoie, S., Azevedo, R. (2023). Measuring Multidimensional Facets of SRL Engagement with Multimodal Data. 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_10

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