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Wearable positioning sensors are enabling unprecedented opportunities to model students’ procedural and social behaviours during collaborative learning tasks in physical learning spaces. Emerging work in this area has mainly focused on modelling group-level interactions from low-level x-y positioning data. Yet, little work has utilised such data to automatically identify individual-level differences among students working in co-located groups in terms of procedural and social aspects such as task prioritisation and collaboration dynamics, respectively. To address this gap, this study characterised key differences among 124 students’ procedural and social behaviours according to their perceived stress, collaboration, and task satisfaction during a complex group task using wearable positioning sensors and ordered networked analysis. The results revealed that students who demonstrated more collaborative behaviours were associated with lower stress and higher collaboration satisfaction. Interestingly, students who worked individually on the primary and secondary learning tasks reported lower and higher task satisfaction, respectively. These findings can deepen our understanding of students’ individual-level behaviours and experiences while learning in groups. 

Learning analytics uses large amounts of data about learner interactions in digital learning environments to understand and enhance learning. Although measurement is a central dimension of learning analytics, there has thus far been little research that examines links between learning analytics and assessment. This special issue of Computers in Human Behavior highlights 11 studies that explore how links between learning analytics and assessment can be strengthened. The contributions of these studies can be broadly grouped into three categories: analytics for assessment (learning analytic approaches as forms of assessment); analytics of assessment (applications of learning analytics to answer questions about assessment practices); and validity of measurement (conceptualization of and practical approaches to assuring validity in measurement in learning analytics). The findings of these studies highlight pressing scientific and practical challenges and opportunities in the connections between learning analytics and assessment that will require interdisciplinary teams to address: task design, analysis of learning progressions, trustworthiness, and fairness – to unlock the full potential of the links between learning analytics and assessment.