International Journal of Computer-Supported Collaborative Learning

In this paper, and as a tribute to our friend and collaborator Barbara White, we explore how Generative AI (GenAI) technology can create stimulating new learning environments that support complex sense-making activities. We present a case study of expert use of a chat-based generative AI tool to examine the feasibility of using human–computer collaborative interactions to support metacognition and sociometacognition, i.e., knowledge about, awareness of, and ability to regulate individual (meta) and collective (sociometa) cognition. Our questions are: (RQ1) Is it possible for human–GenAI collaborative interactions to support metacognition, and (RQ2) Is it possible for them to support sociometacognition, i.e., knowledge about, awareness of, and ability to regulate individual (meta) and collective (sociometa) cognition. Our initial findings, though limited by the exploratory, case-based methods used, indicate the promise of GenAI as a valuable social interaction and cultural simulation tool for learners to practice collective sensemaking skills. Although the limitations of chat-based GenAI technologies, including their tendency to provide definitive answers unsupported by evidence, are worth mentioning, our findings contribute to the ongoing conversations around how to develop technologies to support learners’ argumentation practices. Accordingly, this study has important implications for future research and practice on using chat-based GenAI as a partner for students to practice the knowledge and skills connected to argumentation and scientific claims, especially in larger courses or broader audiences.

While there are many documented approaches to using technological tools to support collaboration in remote environments, studies related to proof-based courses are overwhelmingly situated in the context of geometry. This study uses instrumental genesis theory to study how students in an introduction to proofs course operationalize the technological tools, namely Google Docs and Zoom, available to them to engage in collaborative proof activity during small group work. Results from our analysis found that students coordinate uses of different tools to develop instruments that can be used to (1) engage in collective argumentation by coordinating visual mediators and verbal communication and (2) co-construct a group solution by refining shared text. In particular, Google Docs was found to be a versatile and rich tool that supported the students’ collaborative activity and encouraged a more active approach to proof-related writing. We discuss implications of the students’ tool use on their collective mathematical proof activity.

Teaching with virtual worlds provides new means for collaborative learning but creates challenges for teachers in terms of IT skills. To address these challenges, we developed a teaching model for using virtual worlds in classroom practices and applied it to Minecraft in several rounds of design-based research experiments. Our conceptual framework combines ideas from software engineering (sociotechnical congruence) and social sciences (intersubjectivity and emergence). Empirically, we addressed the problem of how shared understanding evolves in computer-mediated learning activities. We video-recorded classroom activities and analyzed them using interaction analysis. The teaching model engaged the students in two interdependent processes, referred to as objects: (1) a social object (discussions) that led to a shared knowledge object (video-recorded role-play) and (2) a technology object (Minecraft buildings) for staging the role-play. Our findings include an empirical phenomenon that we call emergent group understanding, which arose from the complex social interactions between social and technology objects when Minecraft was used as a virtual world in a social studies classroom. This revealed two connected subprocesses: (1) a spontaneous act of providing information to assist learners in contextualizing their actions and interactions against a common background, and (2) setting localized goals to guide future actions and interactions. This finding extends previous research by identifying fine-grained processes of intersubjectivity that contribute to collaborative learning. More generally, our teaching model addresses the problem of balancing creative and instructional learning goals.

Collaborative learning in high-fidelity simulators is an important part of how master mariner students are preparing for their future career at sea by becoming part of a ship’s bridge team. This study aims to inform the design of multimodal learning analytics to be used for providing automated feedback to master mariner students engaged in collaborative learning activities in high-fidelity navigation simulators. Through a design ethnographic approach, we analyze video records of everyday training practices at a simulator center in Scandinavia, exploring (a) how feedback is delivered to students during collaborative activities in full-mission simulators and (b) which sensors are needed and why they are needed for capturing the multimodal nature of professional performance, communication, and collaboration in simulation-based collaborative learning. Our detailed analysis of two episodes from the data corpus shows how the delivery of feedback during simulations consists of recurring, multidimensional, and multimodal feedback cycles, comprising instructors’ close monitoring of student’s actions to continuously assess the fit between the learning objectives and the ongoing task. Through these embedded assessments, feedback that draws on the rich semiotic resources of the simulated environment, while considering aspects of realism and authenticity, is provided. Considering the multidimensional and multimodal nature of feedback in professional learning contexts, we identify technologies and sensors needed for capturing professional performance in simulated environments.

Collaborative skills are crucial in knowledge-rich domains, such as medical diagnosing. The Collaborative Diagnostic Reasoning (CDR) model emphasizes the importance of high-quality collaborative diagnostic activities (CDAs; e.g., evidence elicitation and sharing), influenced by content and collaboration knowledge as well as more general social skills, to achieve accurate, justified, and efficient diagnostic outcomes (Radkowitsch et al., 2022). However, it has not yet been empirically tested, and the relationships between individual characteristics, CDAs, and diagnostic outcomes remain largely unexplored. The aim of this study was to test the CDR model by analyzing data from three studies in a simulation-based environment and to better understand the construct and the processes involved (N = 504 intermediate medical students) using a structural equation model including indirect effects. We found various stable relationships between individual characteristics and CDAs, and between CDAs and diagnostic outcome, highlighting the multidimensional nature of CDR. While both content and collaboration knowledge were important for CDAs, none of the individual characteristics directly related to diagnostic outcome. The study suggests that CDAs are important factors in achieving successful diagnoses in collaborative contexts, particularly in simulation-based settings. CDAs are influenced by content and collaboration knowledge, highlighting the importance of understanding collaboration partners’ knowledge. We propose revising the CDR model by assigning higher priority to collaboration knowledge compared with social skills, and dividing the CDAs into information elicitation and sharing, with sharing being more transactive. Training should focus on the development of CDAs to improve CDR skills.

This study analyzed collaborative invention projects by teams of lower-secondary (13–14-year-old) Finnish students. In invention projects, student teams design and make materially embodied collaborative inventions using traditional and digital fabrication technologies. This investigation focused on the student teams’ knowledge creation processes by examining how they applied maker practices (i.e., design process, computer engineering, product design, and science practices) in their co-invention projects and the effects of teacher and peer support. In our investigations, we relied on video data and on-site observations, utilizing and further developing visual data analysis methods. Our findings assist in expanding the scope of computer-supported collaborative learning (CSCL) research toward sociomaterially mediated knowledge creation, revealing the open-ended, nonlinear, and self-organized flow of the co-invention projects that take place around digital devices. Our findings demonstrate the practice-based, knowledge-creating nature of these processes, where computer engineering, product design, and science are deeply entangled with design practices. Furthermore, embodied design practices of sketching, practical experimenting, and working with concrete materials were found to be of the essence to inspire and deepen knowledge creation and advancement of epistemic objects. Our findings also reveal how teachers and peer tutor students can support knowledge creation through co-invention.

In online learning at scale, wherein instructional videos play a central role, interactive tools are often integrated to counteract passive consumption. For example, the forum or discussion board is widely used, and an emerging functionality, danmaku, which enables messages to be synchronized with video playback, has also been utilized recently. To explore how mass participation is accommodated and what categories of interaction learners implement, this study utilizes analysis of interaction and manual content analysis through learner-generated text data from two specific tools employed in a massive open online course (MOOC) setting: the discussion board (N = 739) and danmaku (N = 2435). Results of the analysis of interaction indicate that mass participation is managed differently by the tools: danmaku fosters a collective space for massive participants, while the discussion board organizes them into threaded small groups. In addition, results of the content analysis show danmaku primarily supports indirect interaction with a focus on the socio-emotional dimension, while the discussion board serves as a platform for direct discussions, particularly in the cognitive dimension. Furthermore, within the context of large-scale engagement, various levels of joint interaction, in addition to collaboration, are discerned and discussed in both socio-emotional and cognitive interactions. The findings offer insights for developing sociable and scalable socio-technical environments in computer-supported collaborative learning (CSCL), addressing emerging educational trends. Practical implications for educational design based on these findings are also discussed.

Team cohesion is critical in driving successful outcomes for teams in collaborative learning settings. It shapes team behaviour, fostering shared perceptions, group synchrony and a common goal-oriented approach. This affinity becomes evident in dynamic interactions, offering insights into team behaviour through interaction data analysis. Interpreting interaction data proves complex, hampering our understanding and insights into shared team perceptions and task cohesion development. This paper used temporal motif analysis to examine the changes in team members’ cohesive perceptions and behaviours, including task cohesion, performance outcomes, engagement and group synchrony. Trace data from an online work-integrated learning environment captured learning behaviours, while responses to a questionnaire at different stages of a study program captured task cohesion and cohesive perceptions. The findings reveal teams with strong task cohesion and high performance tend to share similar cohesive perceptions driven by interdependent interactions. Conversely, teams with different cohesion perceptions have lower interaction interdependence and poorer performance. Through analysing team interaction data, this study uncovered key insights to promote positive adjustments aligning team perceptions, enhancing collaborative learning and offering support for improved performance, engagement and synchrony among teams, ultimately benefiting learning outcomes and the cultivation of skills and competencies.