International Journal of Computer-Supported Collaborative Learning

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.

The formation of effective collaborative programming groups is vital for collaborative knowledge innovation. Previous research has predominantly examined the influence of group composition approaches from a computational perspective, yet there remains a limited resolution of their real-world educational impacts. This study offers empirical insights into the effects of homogeneous versus heterogeneous groups on student performance within collaborative programming contexts. The group composition system was established using a genetic algorithm, with the inclusion of socio-emotional competence, learning styles, and academic achievement. A total of N = 478 students aged between 13 and 15-years-old voluntarily participated in the study and were divided into 42 heterogeneous groups (n = 166), 40 homogeneous groups (n = 163), and 36 random groups (n = 149) with a group size of four. All participants were subjected to identical pedagogical conditions under a double-blinded study design. Collaborative programming performance was assessed both summatively and formatively, incorporating multi-source evidence from teacher observations, student self-reports, and peer evaluation scores. The results indicate that heterogeneous groups notably outperform homogeneous groups and random groups across most measurements. Implications for implementing collaborative programming in real-world classroom settings are provided.

As smart technologies become part of daily life, families face new opportunities and challenges in learning together. This paper introduces FamiData Hub, a speculative computer-supported collaborative learning (CSCL) prototype that supports families in building critical data literacy within smart homes. Through workshops with 17 families, the study explores how collaborative learning emerges through interaction, storytelling, and shared problem-solving, with family roles shifting dynamically. The findings challenge traditional adult-to-child teaching models, proposing instead a multidirectional learning space where anyone—including children and digital tools—can be the “more knowledgeable other.” The study highlights the value of family centered, socially embedded approaches to critical data literacy and offers insights for designing intergenerational CSCL systems to foster critical data literacy.

Mutual engagement, the dynamic process through which collaborators reciprocally take up and sustain one another’s ideas and actions, is crucial to collaborative problem solving (CPS). However, existing research has yet to fully specify concepts or methodologies needed to capture these dynamic characteristics. This gap highlights the need to examine how these patterns evolve across different CPS phases to inform more sophisticated instructional strategies that enhance collaborative learning. This exploratory study integrates multimodal and content analyses to examine phase-sensitive patterns of mutual engagement in small teams. In total, 28 college students participated in video-recorded CPS activities across four distinct phases. The findings revealed that high-performing teams displayed structurally complete elaborative sequences, in which invitations to contribute were taken up, elaborated, and reciprocated. Low-performing teams, by contrast, exhibited fragmented sequences that failed to return to elaboration. These interactional differences co-occurred with distinct multimodal signatures. High-performing teams exhibited greater interest, less frequent neutral emotions, and early posture synchrony, patterns that were especially pronounced during the ideation phase. Conversely, low-performing teams showed lower interest, persistent neutral emotions, and late, reactive posture synchrony. The findings elucidate the socio-cognitive characteristics of mutual engagement and demonstrate the potential for integrating emotional and behavioral indicators for a richer understanding. These insights can inform the design of instructional scaffolding and phase-sensitive support systems to enhance successful collaborative learning.

Productive collaborative discourse requires students to continuously advance ideas, often through the creation, modification, and integration of digital artifacts in a communal space. Without these processes, ideas remain isolated, fragmented, and unable to advance shared understanding. To support such discourse processes, this study proposes a knowledge synthesis (KS) intervention to facilitate a process of creating knowledge syntheses from ideas represented in digital artifacts and then leveraging these knowledge syntheses, represented in new digital artifacts, to deepen student collaboration. To examine the enactment of this intervention in a graduate-level course, we asked: What were the key characteristics of students’ knowledge synthesis artifacts? How did student groups use the synthesis artifacts during their discourse? To what extent did the synthesis artifacts facilitate collaborative discourse? We analyzed multiple data sources—including student-created synthesis artifacts, perception data, classroom video recordings, and co-constructed group artifacts—using a combination of descriptive, content, and interaction analyses. Findings revealed diverse approaches to knowledge synthesis and showed that synthesis artifacts facilitated discourse progression, fostered a range of knowledge practices, and supported the evolution of group artifacts. By promoting knowledge synthesis and examining its role in collaborative discourse, this study contributes to computer-supported collaborative learning (CSCL) by advancing the theoretical understanding of knowledge synthesis and offering pedagogical strategies for supporting this practice in classrooms.

In collaborative learning game environments where competition and collaboration coexist, conflicts among students are not uncommon. While conflicts of ideas and opinions are prevalent during collaborative learning, they are often perceived as elements to be avoided. One of the main concerns about conflict is its ability to trigger negative emotions, such as anger, which can compromise effective peer interaction, collaborative learning, and, in turn, diminish the quality of group discussions. However, this raises the question of whether anger always negatively affects collaborative learning. Most studies on negative emotions are related to test anxiety or boredom, while the impacts of emotions such as anger on learning are less explored. Especially within computer-supported collaborative learning (CSCL), there is limited research on how anger impacts students’ collaborative activities and learning. To address these issues, this paper aims to explore the potential relationship between anger and its impact on students’ collaborative discourse in a hybrid game-based simulation. Our findings suggest that anger has the potential to facilitate diverse and productive collaborative discussions. Students, driven by their anger, delved deeper into game mechanics, linked concepts to real-life situations, and employed various forms of logical reasoning to substantiate their opinions. However, the moment a student exhibited “tilting“ behavior, the quality of their collaborative discussions plummeted. Our findings provide important preliminary insights into the concept of “tilting” within immersive collaborative learning games and how it may manifest; they also offer guidance on the timing for educators’ intervention in collaborative discourse when anger arises among students.