Elementary school students’ strategic learning: does task-type matter?

This study investigated what types of learning patterns and strategies elementary school students use to carry out ill- and- well-structured tasks. Specifically, it was investigated which and when learning patterns actually emerge with respect to students’ task solutions. The present study uses computer log file traces to investigate how conditions of task types that might affect strategic learning. Elementary school students (N = 12) participated in two science study lessons. During these lessons the students were asked to solve well- and ill-structured tasks. For both of these tasks, the students used the gStudy learning environment designed to support strategic learning. In addition, gStudy records traces of each student’s strategic actions as they proceed with tasks. First, the students’ task solutions was rated according to three categories, namely “on track”, “off track” and “partial solution”. Second, learning patterns in terms of learning strategies that emerged throughout these tasks were investigated. Third, detailed cross case analysis was used to explore in depth how and when these learning patterns were used with respect to the students’ task solutions. The results show that young students’ can provide in-depth task solutions, but also adapt to the task complexity. However, despite the task types being different, the students had same types of learning patterns. The detailed cross-case comparison of the students’ task solutions with respect to learning patterns indicates that there are intra individual differences concerning how students allocate their learning strategy use. Especially if the task is ill-structured, it can also mislead the students to focus on irrelevant aspects and hinder strategic learning.     

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

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 be utilised to predict learning outcomes in collaborative learning.

Practitioner notes

What is already known about this topic

• Socially shared regulation has been recognised as an essential aspect of collaborative learning success.
• It is challenging to make the processes of learning regulation ‘visible’ to better understand and support student learning, especially in dynamic collaborative settings.
• Multimodal learning analytics are showing promise for being a powerful tool to reveal new insights into the temporal and sequential aspects of regulation in collaborative learning.

What this paper adds

• Utilising multimodal big data analytics to reveal the regulatory patterns of shared physiological arousal events (SPAEs) and regulatory activities in collaborative learning.
• Providing evidence of using multimodal data including physiological signals to indicate trigger events in socially shared regulation.
• Examining the differences of regulatory patterns between successful and less successful collaborative learning sessions.
• Demonstrating the potential use of artificial intelligence (AI) techniques to predict collaborative learning success by examining regulatory patterns.

Implications for practice and/or policy

• Our findings offer insights into how students regulate their learning during collaborative learning, which can be used to design adaptive supports that can foster students' learning regulation.
• This study could encourage researchers and practitioners to consider the methodological development incorporating advanced techniques such as AI machine learning for capturing, processing and analysing multimodal data to examine and support learning regulation.