Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics

Abstract

Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace-based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace-based approach to study SSRL, there remains a paucity of evidence on how trace-based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace-based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace-based data could

Computational Learning Analytics to Estimate Location-Based Self-Regulation Process of Real-World Experiences

“…How a learner purposefully self-regulates his/her learning processes to accomplish the learner's own learning goals [14], [15] is high-level assessment information hard to trace from the outside of the learner [16]. For example, during real-world learning, a learner should self-regulate to re-consider where, what, and how to learn in a wide and uncontrolled field-study area when his/her grounded cognition does not work well to find useful information from the surroundings.…”

“…For designing learning analytics to trace a complex selfregulated process of learning, it is indispensable to integrate theoretical models and frameworks from multiple disciplines including educational and computational sciences [16]. In this section, we discuss how to integrate and enhance the theories of self-regulated learning (i.e., internal autonomous computation), grounded cognition (i.e., real-world oriented cognition), computational behavior modeling (i.e., behaviorbased decision-making system), and research design (e.g., science of natural behavior).…”

“…Traditionally, in various educational settings (e.g., mathematical learning [28], language acquisition [29], engineering education [30], learning from others [31], inquirybased classroom learning [32], mastery-based online learning [33]), a strategy is considered as a way to solve a problem or to achieve a particular intellectual achievement (e.g., way of inference, problem solving, surveying, collaboration, self-regulation) [4]. Recent studies on individual-level selfregulation of learning (SRL) and group-level self-regulation of learning (i.e., socially shared regulation of learning; SSRL) have maintained a focus on learning in indoor settings (e.g., classrooms, online, museums) [15], [16], [34], rather than realworld learning.…”

Learning analytics and assessment: Emerging research trends, promises and future opportunities

“…Moreover, in one of the study scenarios, the authors documented significant and substantial correlation between students' spatial-procedural behaviours extracted from indoor positioning data and instructor's assessment of student performance in the task. Nguyen et al (2022) examined student electrodermal activity and video data to gain deeper insight into regulatory activities in collaborative learning settings. The results indicate differences in regulatory patterns between successful and less successful collaborative sessions, and also provide evidence that multimodal data collected in this study can be utilised to predict student success in collaborative learning with considerable accuracy.…”

“…Temporal and fine‐grained analytics approaches presented in this study may be a promising venue towards advancing dynamic and theory‐aligned assessments of computer‐supported collaborative learning. Yan et al (2022) and Nguyen et al (2022) entertained multimodal learning analytics approaches to assess student teamwork and performance in collaborative learning tasks. Yan et al (2022) analysed student indoor positioning data and detected differences in spatial‐procedural behaviours between low‐ and high‐performing teams in a simulation‐based learning task.…”