What multimodal data can tell us about the students’ regulation of their learning process?

By Sanna Järvelä, Jonna Malmberg, Eetu Haataja, Márta Sobocinski, Paul A. Kirschner. Learning and Instruction, 2019

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Continuing research has provided increased understanding of the complex processes involved in self-regulated learning as a cyclical and metacognitive process involving adaptive thinking, motivation, emotion, and behavior (Zimmerman & Schunk, 2011). In addition, the increased focus on the social and interactive nature of learning has led to research that provides a theoretical understanding of self-regulation as a socio-cognitive phenomenon. Hadwin, Järvelä, & Miller, (2018) and Järvelä and Hadwin, (2013) define and conceptualize three forms of learning regulation (i.e., self-regulation of learning [SRL], co-regulation of learning [CoRL], and socially shared regulation of learning [SSRL]) as central processes in highly interactive and collaborative learning contexts. Although there has been significant theoretical and conceptual progress in social aspects of SRL theory, there has been little progress in developing methods that make the invisible mental regulation processes and their accompanying social and contextual reactions visible and, thus, measurable. “Making visible” means that even though SRL is a psychological phenomenon (Winne, 2017), it has physiological indicators such as stress, excitement, enthusiasm, or emotional dynamics in groups (Mønster, Håkonsson, Eskildsen, & Wallot, 2016). Traditional approaches to measuring the regulation of learning are based on selfreports or other subjective measures (e.g., subjective coding of video and/or verbal protocols) that poorly inform the deployment of regulatory processes in social learning contexts. Self-reports are based on students' perceptions of how they would or did enact certain processes, but these perceptions often do not align with what actually occurred during learning (Winne, 2004; Zimmerman, 2008). Subjective coding of observation data is also weak due to the coders' interpretations of observed behaviors. Another problem is that its results lack generalizability as they are both content and situation specific and time dependent (Strijbos & Fischer, 2007). Triangulating multiple sources of data could overcome these methodological weaknesses. Extending data collection to physiological reactions can provide information about a person's physiological reaction to a situation, which may be linked with their thoughts, but which cannot provide direct information about what a person is thinking (Azevedo, 2015). Triangulation involves matching process data resulting from different channels based on the timestamped information related to each data source. For example, video data-coding can be the foundation for contextualizing process data analysis and triangulation (Järvelä, Järvenoja, Malmberg, Isohätälä, & Sobocinski, 2016). This triangulation enables testing and experimentation with different combinations of data to investigate and eventually validate the appropriateness of different data channels for SRL. Recent advances in the development of new data-capturing devices allow researchers to go beyond ontologically flat data (not stratified) of tracking, coding and modelling of basic actions and processes observable behaviors to multimodal data-sets that simultaneously trace a range of cognitive and non-cognitive processes to more nuanced ways, including nearly invisible micro-level environmental interactions and invisible responses of the body and the brain (Reimann, Markauskaite, & Bannert, 2014). These new technologies include eye-movement tracking, brain activation, skin conductance, and other bio-physiological signals. As Reimann et al. (2014) argued, capturing process data across the modalities of the body, brain, actions, and language provides us with resources for exploring learning processes that cross the ontological boundaries between the human body (i.e., neurobiological processes), the environment (i.e., actions), and the mind (i.e., dispositions). This paper explores what multimodal data can reveal about SRL processes in collaborative learning tasks with an emphasis on how data triangulation can help the study of important features of regulated learning and solve several current methodological limitations.

Subfields: Developmental and Educational Psychology, Education, Language and Linguistics

View Paper: https://www.semanticscholar.org/paper/7f957cc02eef28908d9d01b8612a17a209823f3b