International Journal of Computer-Supported Collaborative Learning

Collective emotions are macro-level phenomena arising from the emotional interactions among individuals in shared situations, which dynamically evolve through the process of interpersonal interaction. Although collective emotions are closely related to group learning performance and collaboration outcomes in computer-supported collaborative learning (CSCL), existing research often neglects the emotional states of nonspeakers and has insufficiently explored the interpersonal emotional interaction patterns among learners. In addition, the distinctive features of interpersonal interactions are rarely considered in the analysis of collective emotions’ evolution. The rapid advancement of AI technology enables more detailed and cost-effective emotion capture. This study uses AI to simultaneously capture the speakers’ verbal expression of emotions and the nonspeakers’ facial expression of emotions in a CSCL context. Using epistemic network analysis (ENA) and lag sequential analysis (LSA), this study systematically compares the interpersonal interaction patterns and temporal evolution of collective emotions between high- and low-performance groups. The results reveal significant differences between the groups: in terms of interpersonal interactions, the high-performance group typically exhibits neutral or cognitively engaged facial expressions from nonspeakers when the speaker expresses a positive viewpoint, whereas the low-performance group shows more confusion and negative emotions from the speaker, with nonspeakers frequently responding with affiliative smiles. In terms of temporal evolution, the high-performance group demonstrates a productive emotional transition path, while the low-performance group shows a negative emotional trajectory and a vicious cycle of negative emotions. This study fills a gap in the CSCL field regarding the interpersonal mechanisms of collective emotions and provides empirical evidence for precise teaching interventions and instructional design.

Collaborative problem solving (CPS) and mathematical reasoning are both crucial twenty-first-century competencies. Research suggests that aspects of CPS can benefit mathematics learning. At the same time, particularly in disciplinary settings, successful CPS may require facilitator support alongside students’ social and cognitive skills. This sequential explanatory mixed-methods study investigates the impacts of human facilitation on supporting CPS during online small-team collaboration in mathematics. Using learning analytics techniques such as epistemic network analysis and sequential pattern mining, we analyzed and compared chat logs from human facilitated and unfacilitated teams in high school classrooms. This quantitative analysis identified frequent sequences of facilitator moves and CPS behaviors, as well as sequences of CPS behaviors distinguishing human facilitated and unfacilitated chats. We then analyzed chat episodes featuring these sequences for the role of facilitation in mathematical problem-solving processes. Results indicated that human facilitation effectively promoted constructive behaviors such as multiple turns of social negotiation, while reducing inappropriate communication. Different facilitation strategies, such as soliciting disagreement or negotiation, elicited targeted CPS behaviors and promoted mathematical reasoning and explanation, likely contributing to improvements in team performance. Findings have practical implications for facilitating CPS in CSCL mathematics settings.

Deep interaction is pivotal for fostering higher-order knowledge construction in online collaborative learning. Yet, current practice often suffers from shallow exchanges and inefficient collaboration, which severely constrains advances in collaborative knowledge building. To address this challenge, this study conducted three iterative design cycles to develop and refine a teacher–student interaction framework comprising three phases: individual knowledge construction, teacher-guided argumentation, and learning-community-based co-creation. Adopting a design-based research approach, this study examined the framework’s effectiveness through three rounds of iterative teaching experiments. Using a mixed-methods design that integrated social network analysis and content analysis, this study focused on the implementation effects of several support strategies, including a four-component topic design, selective teacher interventions, a role-playing mechanism, and a What, Why, Where, Which, Who (5W) reply model. The results suggest that the implementation of the framework was associated with increased social interaction density, improved quality of discussion posts, and a progression in collaborative knowledge building from simple information sharing toward negotiated consensus and co-creation. The teacher’s role evolved dynamically from peripheral participant to central leader and then to a supportive node, thereby illuminating principles for the adaptive enactment of teaching presence in collaborative learning. This study offers contextually grounded design principles and a detailed practice case that may inform the design and optimization of teacher–student interactions in similar online collaborative learning environments.

Pair programming (PP) has been proposed as an efficacious pedagogical strategy in computing education to enhance students’ coding skills and cooperation abilities. However, the impact of different pairing modes on cooperation and programming performance remained unclear. Understanding how prior knowledge influences neural and behavioral dynamics in PP is crucial for optimizing cooperative learning strategies. This study investigated how different pairing modes, categorized by prior programming knowledge, affect interpersonal neural synchronization (INS) and programming efficiency in PP. The goal is to uncover neural mechanisms underlying effective cooperation and provide evidence-based insights for CS education. We conducted a functional near-infrared spectroscopy (fNIRS) hyperscanning study with participants assigned to high–high (HH), low–high (LH), and low–low (LL) dyads on the basis of their programming prior knowledge. Neural synchronization was analyzed across key brain regions during PP tasks to assess the cognitive and cooperative processes involved. HH dyads exhibited significantly higher programming efficiency, likely due to better time-management skills. INS results revealed distinct INS patterns across three dyad modes: both the LH and LL groups showed significant INS increases in the right temporoparietal junction (rTPJ), whereas HH dyads showed additional INS increases in the prefrontal cortex (PFC). Higher INS levels correlated with cooperative behaviors that foster mutual understanding and consensus-building. These findings underscore the role of prior knowledge in shaping neural and behavioral dynamics during PP. They provided empirical support for optimizing pairing strategies in programming education, suggesting that effective cooperation relies on both cognitive compatibility and synchronized neural activity.

Computer-Supported Collaborative Learning (CSCL) has grown into a multidisciplinary field, yet persistent conceptual fragmentation limits cumulative knowledge building and newcomer entry. We frame paradigm formation in CSCL as a wicked problem that requires collective action. We report outcomes from the half-day CSCL Paradigm-Building Workshop at the ISLS conference 2025, preceded by anonymous online pre-workshop exchanges. Twenty participants and panel members from diverse disciplines and cultural backgrounds identified priority tensions around defining CSCL, integrating theories, and the future of the field. Across small-group and panel discussions, participants concluded that building a shared CSCL paradigm was neither feasible nor desirable. Participants highlighted weak communication across paradigms and the absence of shared descriptors for phenomena and analytic approaches. To counter the negative effects of co-existing paradigms, participants proposed a community-maintained CSCL taxonomy that tags and guides contributions along key dimensions of collaborative learning, instructional support, context, and epistemic stance. Such an approach will serve both the need to bridge paradigms and continuous community building. We close with a roadmap for collaboratively refining a proposed first draft of such a taxonomy and sustaining paradigm-building through ongoing workshops and community-based research.

Understanding how groups regulate their learning together requires more attention to the verbal and nonverbal cues that shape collaborative activity. This study investigates how gaze and proxemics share and signal socially shared regulation of learning during a classroom-based collaborative inquiry task. In their natural classroom environment, 62 secondary school students worked in small groups during a physics task while video and audio were recorded. Gaze and proxemic behaviors were extracted from standard two-dimensional video through automated computer vision techniques, and challenge and regulation processes were identified using the trigger regulation framework. Transmodal ordered network analysis was then used to examine the temporal relationships among embodied cues and regulatory processes across different spatial configurations and artificial intelligence support conditions. The results show that gaze and proxemics act as functional components of regulation. When groups were physically distant, mutual gaze signaled emerging challenges and preceded monitoring. When groups were physically close, joint attention supported transitions from monitoring to selecting and enacting strategies. Adaptive artificial intelligence support strengthened cycles of shared monitoring, while static support produced more procedural patterns of strategy use. The findings advance understanding of embodied regulation in authentic classrooms and demonstrate a nonintrusive methodological approach for investigating multimodal in situ collaborative learning.

Collaborative digital game-based learning (DGBL) in mathematics education is a well-researched area. While non-game-related social interaction (NGI) has often been viewed as a potential source of distraction to learning, it is an integral part of gameplay. Gaining deeper insight into NGI helps leverage the tension arising from the conflict between learning and gameplay when implementing DGBL and advances scholarly work on the impact of off-task interaction in collaborative learning more broadly. Using a qualitative case study of four students engaged in a collaborative DGBL activity, this study examined how NGI is associated with collaborative learning quality, as inferred from group level flow experiences. The findings indicate that, in the context of groups with pre-established basic positive collaborative relationships, NGI enhances learning quality by sustaining supportive epistemic and relational spaces through a mechanism in coordination of shared affective states.

Multimodal learning analytics (MMLA) has improved our understanding of collaboration quality, yet current approaches often overlook students’ perspectives and fail to provide meaningful, student-centered feedback that enhances collaboration literacy. This study addresses these gaps by investigating students’ perspectives on collaboration quality indicators and their feedback utilization preferences. Through an open-ended survey of 290 university students who used a collaboration analytics (CA) tool over 12 weeks of collaborative activities, we identified key individual- and group-level indicators spanning the process, outcome, and review layers. These indicators articulate how learners conceptualize the interplay between individual behaviors, group dynamics, and collaborative outcomes. Additionally, we identified distinct approaches to real-time and post hoc feedback utilization, highlighting their complementary roles in supporting collaboration literacy. Findings led to the development of a conceptual collaboration literacy analytics framework (CLAF) that integrates evaluation metrics, indicator interdependencies, and feedback mechanisms. The framework captures the dynamic relationship between process and outcome measures, connects individual- and group-level indicators, and incorporates review mechanisms as student-driven quality assurance. By guiding future CSCL research and practice, the framework provides an analytical basis for examining collaboration quality and studying how assessment processes and integrated feedback support collaboration literacy.

This study illustrates how a teacher-researcher conducted content analysis and used its results as one source of evidence to evaluate the overall effectiveness of the strategies she implemented to promote creative idea development deliberately among ten students over 8 weeks of threaded discussions in her Moodle-based writing course. The analysis employed a new coding scheme, the Dialogic Idea Development (DID) model, which (1) assesses the quality of idea development across elaborative, (integrative-) argumentative, and creative levels consistently and explicitly in relational terms; (2) draws on these relational assessments as a direct basis for assessing knowledge construction in text-based discussion forums; and (3) was integrated with statistical techniques within a mixed-methods action research design to explore how the dialogic quality of students’ idea-developing moves evolved collectively over time. The results showed that her strategies appeared effective in encouraging exploratory contributions to a greater extent and across all students but insufficient in supporting or sustaining creative idea development among all in that course. This study offers philosophical, conceptual, and methodological insights into collaborative learning analytics. It represents an early relational attempt to generate valid and reliable inferences about the quality of learning in discussion forums. It also demonstrates how to aggregate these inferences using both contribution-based and contributor-based methods and to synthesize the patterns across iterative action cycles to evaluate the overall effectiveness of the pedagogical decisions aimed at promoting students’ equitable participation in creative knowledge work in educational collaboration.

Collaborative diagnostic reasoning (CDR) is a critical yet cognitively demanding skill in many professional domains and an example for problems that require collaboration for its solution. This study explores how novice diagnosticians—specifically automotive technician trainees—can effectively learn complex collaborative skills such as CDR through computer-supported instruction. Drawing on research on collaborative diagnostic reasoning, example-based learning and cognitive load, we compared two instructional approaches: learning by self-explaining worked examples and learning by problem-solving. Furthermore, we examined how the specificity of self-explanation prompts (specific versus general) of worked examples interacted with learners’ prior CDR skills. In a prepost experiment, 154 trainees (77 dyads) were assigned to one of three learning conditions: worked examples with specific prompts, worked examples with general prompts, and solving problems without worked examples. Knowledge of CDR strategies, quality of the CDR process and outcome, and cognitive load were measured. Our results demonstrated that self-explaining worked examples significantly improved declarative knowledge of CDR strategies and the quality of the process compared with solving problems. However, worked examples did not improve the application of CDR strategy knowledge or reduce cognitive load. Contrary to expectations, problem-solving resulted in a higher quality of the CDR outcome than self-explaining worked examples. The specificity of the prompts demonstrated no significant effects. Overall, our findings suggest that self-explaining worked examples support early stages of learning complex collaboration skills such as CDR, even in short-term interventions, while more supported practice in problem-solving seems necessary for the development of improved procedural skills.

Collaborative knowledge construction (CKC) is the process through which students jointly construct shared understanding and generate new knowledge through interactive discussion and collective reasoning. Scripted roles, as important external scaffolding, have been widely used in CKC to enhance collaborative learning outcomes and promote the learning processes in different learning environments. However, most existing studies have merely applied scripted roles in a single collaborative environment, with limited research exploring their effectiveness in promoting CKC across diverse environments. To address this research gap, this study proposed a scripted role framework (i.e., toastmaster, supporter, opponent, summarizer) and investigated the impacts of the four roles on undergraduates’ CKC processes in different learning environments (i.e., online environment, offline environment). Specifically, this study conducted a 14-week quasi-experiment and used epistemic network analysis and lag sequential analysis to compare students’ viewpoint depth and interaction patterns in four conditions (i.e., online with scripted role group, online without scripted role group, offline with scripted role group, offline without scripted role group). The results showed that the four scripted roles effectively enhanced the depth of CKC, although its efficacy exhibited significant context dependency. Moreover, compared with the online environment, scripted roles proved more effective in facilitating students’ viewpoint depth and deep-level behavioral transformation in the offline environment. Interestingly, there was no significant difference in opponent roles’ viewpoint depth between the two environments, and their behavioral shift exhibited from deep back to superficial interaction. On the basis of the findings, this study further provides empirical evidence for the effectiveness of scripted roles and offers practical implications for their design and implementation in different learning. environments.

Effective socially shared regulation of learning (SSRL) in computer-supported collaborative learning (CSCL) often fails to occur because students lack awareness of their peers’ and groups’ activities, thus leading to unsatisfactory learning outcomes. While both group awareness (GA) support and students’ self-regulated learning (SRL) levels are critical to CSCL, previous research has considered them separately, and investigation of their combined effects, especially on students’ SSRL, remains limited. Addressing these gaps, the present study conducted an 18-week experiment with a two-level factorial design to examine the main and interaction effects of GA support (present versus absent) and students’ SRL levels (high versus low) on perceived SSRL skills, observed SSRL behaviors, group task performance, and individual knowledge achievement. A total of 54 undergraduates enrolled in an Educational Research Methods course were randomly assigned to either a GA+ class (n = 28) or a GA− class (n = 26). The results revealed that: (1) GA support showed significant positive effects on all four measured variables, (2) SRL levels showed no significant main effects on these core measures, and (3) interaction analyses suggested that GA support substantially improved overall perceived SSRL skills among low-SRL students, whereas high-SRL students showed greater gains in the monitoring and adapting dimensions of observed SSRL behaviors. No interaction effect emerged for individual knowledge achievement. On the basis of these findings, several practical implications for facilitating successful collaborative learning are proposed.

Research on online problem-based learning—and computer-supported collaborative learning at large—has mostly focused on either the order of group members’ interactions (using time-oriented methods) or the co-occurrence of interactions (using network methods) within the same collaborative episode, while work on longitudinal dynamics has so far been lagging. In this study, we implement a novel method that combines the advantages of both approaches: the relational and temporal dimensions, which is temporal network analysis. Additionally, to capture changes at different temporal scales, we use sequence analysis and multilevel growth models to study how interactions and patterns unfold across time. Our results showed that students who used interactive socioemotional or regulated constructive patterns were more productive in terms of cognitive and knowledge productivity. Explicit group regulation was infrequent and emerged in response to challenges, questions, or disagreements, often with teacher support. Most groups settled into stable regulatory patterns early on, with limited change over time, and transitions—when they occurred—were usually between similar patterns. Our results also suggest that regulation does not naturally improve with time alone, underscoring the importance of early, targeted instructional support to foster more productive regulatory approaches.

Since the beginning of computer-supported collaborative learning (CSCL) research, collaborative writing has been playing a pivotal role as a tool for learning and knowledge construction. In the study presented here, we ask to what extent large language models may not only assist individuals in their writing processes but also serve as a collaboration partner. For this purpose, we analyzed the writing process of individuals supported by ChatGPT. We introduce the use of recurring n-grams as a means for textual uptake, that is, the extent and granularity with which human writers adopt and adapt artificial intelligence (AI)-generated text. On the basis of the overlaps between the ChatGPT output and participants’ final texts, we identified clusters of text reproducers, integrators, and reconstructors. Participants in these clusters differed not only in their subjective contributions and authorship but also in their prior use of ChatGPT and their affinity toward technology interaction. Referring to the conceptualization of interindividual interactions as uptake events, we suggest that n-grams are adequate means to analyze the uptake process in AI-supported human writing. Our findings show that AI-supported writing comprises distinct uptake patterns that differ systematically in the degree of textual reuse and perceived authorship, thereby revealing varying modes of engagement in human–AI co-writing, ranging from passive uptake of AI-generated text to more active and integrative forms of collaboration.