International Journal of Computer-Supported Collaborative Learning

Background: Online hate speech on social networks and the Internet is an increasingly pervasive phenomenon to which both children and adolescents are exposed. Objective: Our study’s main objective was to ascertain whether collective intelligence can improve their handling of hate speech. Methods: We conducted the study on the Collective Learning platform, comparing results between three groups of Spanish adolescents aged 15–16 years. The groups were of different sizes: one large group (G1, n = 123) and two smaller groups (G2, n = 18; G3, n = 23). Results: The experiment showed that the conditions for the emergence of collective intelligence were met within the large group (G1) but not in the two small groups (G2 and G3). The large group, as a collective, acquired capacities to deal with hate speech; however, this did not occur in the two smaller groups. Conclusions: Our study explains how the emergence of collective intelligence in online environments helps group members acquire a series of competencies. In particular, collective intelligence can help adolescents learn to deal with hate speech.

The increasing availability of multimodal sensing technologies has opened new avenues for studying human interactions. However, there remains a lack of systematic synthesis regarding which multimodal metrics are most predictive of productive collaborations. This study addresses this gap by conducting a systematic literature review of 163 studies published since 2000. Grounded in the theoretical framework of multimodal collaboration analytics (MMCA; Schneider et al., 2022), we examine how different data modalities—verbal, gaze, body, head, log, and physiological—are used to assess collaboration. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework (Liberati et al., 2009), we categorize studies on the basis of the types of collaborative indicators, the metrics extracted from multimodal data, and the methods used to establish relationships between them. We find several gaps, including an over-representation of lab-based studies with small sample sizes, reliance on simplistic individual or group synchrony metrics, and a lack of standard indicators for collaboration. We discuss related Grand Challenges for MMCA, including scaling up research through field-based studies, developing interpretable models that contribute to theory, computing sophisticated sensor-based metrics that better capture the temporal dynamics of interaction, and designing interventions that support collaboration using fine-grained, high frequency sensor data.

Collaborative learning deepens understanding by elaborating knowledge and facilitating memory-related information processing through interactions with others. In computer-supported collaborative learning (CSCL), mechanisms identified in collaborative learning are scaffolded through tools such as group awareness and scripted collaboration. While collaborative learning is considered effective, it remains unclear how older adults learn in collaborative environments using concept maps, and how cognitive decline may hinder their learning. Therefore, this study investigates differences between younger and older adults in collaborative learning with concept maps, focusing on learning performance, concept map performance, and the collaborative learning process. Learning performance was assessed using test scores, concept map performance through concept map evaluations as a tool for externalizing knowledge, and the collaborative process using the Interactive-Constructive-Active-Passive (ICAP) framework, which captures cognitive engagement. Results showed that younger adults had higher learning performance than older adults, while older adults showed no significant improvement, indicating a lack of learning gain. Similarly, younger adults outperformed older adults in concept map performance, and no improvement was observed in older adults for concept map scores. This suggests that older adults found it more difficult to elaborate knowledge, such as integrating new information. Regarding the collaborative learning process, younger adults were more likely to engage at the active, while older adults showed higher engagement at the constructive and interactive levels. Epistemic network analysis (ENA) revealed stronger connections between constructive and interactive behaviors in younger adults, and between active and interactive behaviors in older adults. These findings suggest that while younger adults progressively deepen their engagement during collaborative learning, older adults may require the reactivation of memory to engage in elaboration. These results offer insights into designing effective CSCL environments tailored to the learning needs of older adults.

Dialogic education is largely advocated as a means to promote dialogue and reduce polarization. Chatbots based on large language models (LLMs) carry the potential to scale dialogic education by serving as conversation partners and sustaining a dialogic space on various topics. They combine human-like conversational abilities with machine patience. To explore this potential, we fine-tuned an LLM-based chatbot, LlamaLo, using a corpus of productive discussions. We analyzed ten discussions with LlamaLo on contentious topics, such as liberalism and cultural appropriation. Our findings show that LlamaLo effectively opens dialogic spaces by questioning interlocutors’ assumptions, presenting alternative perspectives, and providing relevant knowledge. However, challenges, such as negative tone and bias, could undermine the dialogic space and should be addressed computationally and pedagogically. We conclude that dedicated LLM-based chatbots have the potential for enhancing dialogic education and enabling seamless scripting responsive to real-time needs.

Understanding how multiple dimensions of learning engagement co-develop during collaborative programming remains a critical challenge. Drawing on the four-dimensional engagement framework encompassing behavioral, cognitive, emotional, and social components, this study employs multimodal learning analytics (MMLA) to investigate the dynamic interplay among engagement dimensions, prior knowledge, and leadership type in a university-level collaborative programming course. Group-level ICAP (Interactive, Constructive, Active, Passive) modes were coded from multimodal interaction data, while learning engagement was assessed via integrated behavioral, cognitive, emotional, and social indicators. Findings reveal that prior knowledge supports individual task execution but contributes to collaborative engagement only under strong leadership. The three core dimensions of learning engagement—behavioral, cognitive, and social—were found to be significantly interrelated, reflecting a tightly coupled system of action, thinking, and peer interaction. In contrast, emotional engagement showed weak or inconsistent correlations with the other dimensions. Furthermore, interactive discourse fostered richer engagement and higher achievement, with group formation strategies moderating these effects through the interplay of prior knowledge and leadership type. While ICAP modes and overall learning engagement were strongly correlated—partly reflecting overlapping cognitive components—ICAP captured discourse patterns, whereas engagement scores aggregated multidimensional indicators across the task, suggesting complementary rather than redundant constructs. These findings advance engagement research by integrating discourse classification and multidimensional profiling, offering practical guidance on group formation, scaffolding, and real-time engagement monitoring to enhance collaborative programming pedagogy.

With the rapid development of emerging technologies in education, this research explored how children use teacher-, peer-, and technology-provided feedback together toward collective and embodied learning in a mixed-reality environment. In this study, we investigated how young children interact with feedback in a mixed-reality environment, Science through Technology Enhanced Play (STEP), a system that tracks students’ movement and turns their embodiments into characters on a shared screen. We used coding and interaction analysis to analyze data from three episodes from a curriculum about states of matter across two research sites. Our analysis demonstrated that as the curriculum progressed, children engaged with collective and blended feedback, i.e., input that leads to collective sense-making and liminal blending of multiple sources, in ways that enhanced collective agency over their inquiry. First, we focused on how children transitioned from individualized views of feedback to more collective views by blending multiple sources of feedback (from self, peers, teachers/researchers, and technology) to make sense of solid bonds. Second, we found that the children leveraged feedback from their peers, facilitators, and technology-provided representations to explore how the particles must behave collectively to form liquid bonds. Third, we saw how children engaged with feedback differently on the basis of their role (observing versus embodying) in an activity focused on making gas bonds. More than simply demonstrating the sophistication with which young children engage in collective inquiry-based learning through embodied and technology-enhanced play designs, our work also demonstrates how future learning environments with complex feedback structures (i.e., the coordination of multiple sources and multiple modalities by children working collaboratively) can be designed to support student inquiry and young children’s agency in blending feedback sources that they determine enhance their collective sense-making.

Hands-on laboratories are essential to acquire skills in education. However, they can be costly, lack flexibility, and do not allow one to do an unlimited number of experiments. Virtual and remote laboratories represent an interesting alternative to traditional hands-on lab sessions. On the other hand, fostering collaboration between learners and between learners and teachers is an important aspect to develop in these virtual and remote laboratories, as it enhances learning. This systematic literature review presents an extensive overview of previous research about fostering collaboration in educational virtual and remote laboratories. Results of this study show that communication and group awareness tools are generally well integrated into remote and virtual laboratories. These tools foster collaborative learning as they enable users to communicate, to be aware of the presence and the actions of the other members of the group and to share knowledge. However, tools for guiding and regulating collaboration are poorly integrated in the laboratories. These tools are yet useful to foster collaborative learning as they respectively give instructions to collaborate effectively and information about the state of collaboration to regulate it. This review also identified a minority of studies that assessed the quality of collaboration and learning in laboratories. Future research should put more emphasis in investigating guidance and regulation tools, as well as integrating studies to evaluate collaboration and learning in educational remote and virtual laboratories.

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.