International Journal of Computer-Supported Collaborative Learning

This study investigates how collaboration is practically accomplished on large-scale online platforms, with scale understood qualitatively as asynchronous and fluid participation. Using Stack Overflow as an empirical case, it specifically examines how users collaboratively frame programming problems through questions, comments and iterative edits. Drawing on the practice-based perspective and ethnomethodology, the study uses trace ethnography and sequential analysis of selected Stack Overflow threads. Findings reveal that profession-specific shared objects (minimal reproducible examples) structured within the platform’s dual-space design, consisting of distinct question and commenting spaces, serve as crucial resources, enabling both immediate and future unknown contributors to understand and effectively engage in problem faming and problem-solving processes. Furthermore, the study identifies key interactional methods, i.e., standardized norm-enforcing requests and explicit referencing, which ensure mutual intelligibility of users’ comments and edits, essential for accomplishing collaboration at scale. The findings contribute to theoretical understandings of mass collaboration, offer design insights for platforms to facilitate the coordination of collaborative activities and provide recommendations for professional education to support productive participation in large-scale collaboration.

Effective group formation is an indispensable yet challenging aspect of classroom-based collaborative learning. While existing group formation algorithms show promising computational performance in controlled settings, their practical impact on diverse, real-world classrooms remains underexplored. This paper presents a mixed genetic algorithm integrated into a data-driven learning platform designed to accommodate both homogeneous and heterogeneous student characteristics simultaneously. Implemented in a senior high school EFL classroom, the approach leverages active reading marker logs for data-driven grouping. It incorporates a WordCloud tool to enhance educators’ and learners’ understanding of group composition. Empirical results indicate that this system improves vocabulary learning, and the marker-based grouping strategies positively influence group learning dynamics. These findings underscore the algorithm’s practical relevance and highlight the benefits of interpretable, adaptive group formation methods for authentic educational contexts.

Understanding individual participation is critical for uncovering how individuals learn in collaborative learning as well as for providing personalized support to scaffold team success and individual gains. Modelling individual participation requires a process-oriented method rather than an outcome-focused approach. There is a need for a theoretical framework guiding the collection and analysis of process data for gauging individual participation in collaborative learning. This systematic review synthesizes theoretical aspects and analytical methods for modelling individual participation using process data in collaborative learning. It analyzes 66 studies published between 2005 and 2024, identified through the PRISMA process. Based on the analytical results, we propose a new theoretical framework, COMPAS, consisting of six components to model the multi-faceted and multi-level nature of individual participation in collaborative learning processes: Cognitive interactions, Coordinative interactions, Metacognitive interactions, Passive participation, solo Active participation, and Socio-emotional interactions. The six forms of individual participation were studied using various forms of collaborative learning process data—including oral conversational data, textual input data, log data, and non-verbal physical data—with analytical methods primarily involving descriptive analysis, content analysis, network analysis, and clustering. The synthesized factors influencing individual participation reflect a bi-directional relationship between individual participation and group performance in collaborative learning. This study contributes a new theoretical framework for understanding different forms of individual participation in collaborative learning, as well as highlights the need for and importance of multimodal process data in collaborative learning analytics.

This study qualitatively develops further understandings regarding knowledge and identity construction within computer-supported collaborative learning (CSCL) research by applying discourse analysis and the dialogical self theory (DST) to investigate the role of interpersonal and intrapersonal voices in facilitating knowledge construction. We analyzed and compared the audio recordings of ten students separated into two groups of five (group A and group B) as they engaged in dialogue to construct knowledge for a learning task on physics in a CSCL environment. We divided the dialogue of each group into dialogues by identifying their discourse functions (DF) on the basis of interactional events related to knowledge construction. We then grounded the I-positions of I–it, I–me, I–you and I–we at the utterance level so that we could visualize and describe them within the dialogues that were relevant during knowledge construction in each group. Results showed that the process of knowledge construction for collaborative learning related to DF as well as their use of I-positions. Group A, who failed the learning task, often employed interpersonal voices (I–it/I–me) in dialogues that focused on reviewing directions and strategies at the individual level rather than collective clarification and elaboration or empirical evaluation of knowledge. Group B, who succeeded with the learning task, employed both interpersonal (I–it/I–me) and intrapersonal voices (I–you/I–we) with intrapersonal voices peaking in usage during collective empirical evaluation of knowledge. Our findings underscore prior research that CSCL involves not only knowledge but also identity negotiation as well as demonstrating that DST can aid this exploration.