Collaboration Analytics — Current State and Potential Futures

Abstract

This special issue brings together a rich collection of papers in collaboration analytics. With topics including theory building, data collection, modelling, designing frameworks, and building machine learning models, this issue represents some of the most active areas of research in the field. In this editorial, we summarize the papers; discuss the nature of collaboration analytics based on this body of work; describe the associated opportunities, challenges, and risks; and depict potential futures for the field. We conclude by discussing the implications of this special issue for collaboration analytics.

Examining the effect of a genetic algorithm-enabled grouping method on collaborative performances, processes, and perceptions

“…Most previous studies focus on the evaluation of groups’ summative performance rather than the process-oriented analytics (Lambić et al, 2018 ; Lin et al, 2016 ; Moreno et al, 2012 ; Takači et al, 2017 ), which may result in a lack of a comprehensive, holistic understanding of the CSCL process. There is an analytical trend in the learning science and collaborative learning fields to examine both performance and process data and to reveal the multi-dimensional characteristics of CSCL (Janssen et al, 2013 ; Schneider et al, 2021 ). The multi-method approaches have been used to conduct the multi-dimensional analyses of collaborative learning processes, such as using the statistical analysis, sequential analysis, and social network analysis approaches to investigate the sequences of students’ knowledge contributions (Chen et al, 2018 ) and social interaction structures (Ouyang, 2021 ; Ouyang & Chang, 2019 ; Ouyang & Scharber, 2017 ).…”

“…Taking a step forward, this research took cognitive engagement analysis as an additional supplement to further explore the collaborative process. Furthermore, Schneider et al ( 2021 ) stated that collaborative learning analytics is the intersection of collaborative learning and learning analytics, which expands the learning analytics method to focus on analytics of groups of students. Overall, using multiple learning analytics methods, collaborative analytics can provide feedback on similarities and differences of CSCL results at the group level to improve the understanding of groups’ collaborative work.…”

Exploring the Utility of Social-Network-Derived Collaborative Opportunity Temperature Readings for Informing Design and Research of Large-Group Immersive Learning Environments

“…For small-group learning scenarios, some researchers have looked for ways to turn these physical analytics into markers of collaboration by examining the alignment or synchrony of learners' activity (Cukurova et al, 2018;Reilly et al, 2018;Schneider & Blikstein, 2015). Just recently, there has been a new push for creating a domain of "collaborative analytics" (Schneider et al, 2021). Most researchers use some theoretical understanding about collaborative processes to transform raw data into information about collaboration.…”

Toward capturing divergent collaboration in makerspaces using motion sensors

“…While sensing technology is routinely used by makerspace users to create innovative projects (Cline et al , 2020), it is rarely used to capture data on processes that contribute to learning. Over the past decade, however, there has been a renewed interest in capturing social interactions using sensing technology (sometimes referred to as multimodal collaboration analytics [MMCA], Schneider et al , 2021a). This is because sensing technology has recently become more affordable and accessible, for example, through advanced computer vision algorithms (Hassan et al , 2021).…”