System design for using multimodal trace data in modeling self-regulated learning - Cloude, Elizabeth Brooke; Azevedo, Roger; Winne, Philip H.; Biswas, Gautam; Jang, Eunice E. (Frontiers in Education, 2022)
\begin{abstract} Self-regulated learning (SRL) integrates monitoring and controlling of cognitive, affective, metacognitive, and motivational processes during learning in pursuit of goals. Researchers have begun using multimodal data (e.g., concurrent verbalizations, eye movements, on-line behavioral traces, facial expressions, screen recordings of learner-system interactions, and physiological sensors) to investigate triggers and temporal dynamics of SRL and how such data relate to learning and performance. Analyzing and interpreting multimodal data about learners’ SRL processes as they work in real-time is conceptually and computationally challenging for researchers. In this paper, we \added{discuss recommendations for building a multimodal learning analytics architecture} for advancing research on how researchers or instructors can standardize, process, analyze, recognize and conceptualize (SPARC) multimodal data in the service of understanding learners’ real-time SRL and productively intervening learning activities with significant implications for artificial intelligence capabilities. Our overall goals are to (a) advance the science of learning by creating links between multimodal trace data and theoretical models of SRL, and (b) aid researchers \added{or instructors} in developing effective instructional interventions to \deleted{assist educators in helping} assist learners in developing more productive SRL processes. As initial steps toward these goals, this paper (1) discusses theoretical, conceptual, methodological, and analytical issues researchers\added{ or instructors} face when using learners’ multimodal data generated from emerging technologies; (2) \added{provide an elaboration of theoretical and empirical psychological, cognitive science, and SRL aspects related to the sketch of the visionary system called SPARC that supports analyzing and improving a learner-instructor or learner-researcher setting using multimodal data;} and (3) discuss \deleted{how this novel framework to design and implement SPARC using artificial intelligence and learners’ multimodal data as a platform to make inferences about SRL processes and policies for enhancing instructional interventions.} implications for building valid artificial intelligence algorithms constructed from insights gained from researchers \added{and SRL experts, instructors, and learners SRL via multimodal trace data.} \deleted{understanding of learners' SRL via multimodal trace data.}