International Journal of Computer-Supported Collaborative Learning

The acquisition of online interaction competencies is an important learning objective. The present study explored the relationships between the first-language heterogeneity of computer-supported collaborative learning (CSCL) groups and the development of students’ online interaction self-efficacy via a pretest–posttest design in the context of a nine-week CSCL course. The research participants were 1525 freshmen receiving distance education who were randomly assigned to 343 CSCL groups. Independent of their own language status, students in CSCL groups featuring first-language heterogeneity exhibited lower precourse–postcourse gains in online interaction self-efficacy than students in groups without heterogeneity. Consistent with a theoretically derived moderation model, the relationships between first-language heterogeneity and self-efficacy gains were moderated by the amount of time that the groups spent on task-related communication during the initial collaboration phase (i.e., the relationships were significant when little time was spent on it but not when a great deal of time was spent on it). In contrast, the amount of time that groups spent on communication related to getting to know each other was ineffective as a significant moderator. Follow-up analyses indicated that time spent getting to know each other in first-language heterogeneous CSCL groups seems to have had the paradoxical effect of increasing rather than decreasing perceptions of heterogeneity among group members. Apparently, this effect impaired online interaction self-efficacy gains.

Generative Artificial Intelligence (AI) tools, such as ChatGPT, have received great attention from researchers, the media, and the public. They are gladly and frequently used for text production by many people. These tools have undeniable strengths but also weaknesses that must be addressed. In this squib we ask to what extent these tools can be employed by users for individual learning as well as for knowledge construction to spark a collective endeavor of developing new insights. We take a social, collective notion of knowledge as a basis and argue that users need to establish a dialog that goes beyond knowledge telling (simply writing what one knows) and stimulates knowledge transformation (converting knowledge into complex relational argumentation structures). Generative AI tools do not have any conceptual knowledge or conscious understanding, as they only use word transitions and rely on probabilities of word classes. We suggest, however, that argumentative dialogs among humans and AI tools can be achieved with appropriate prompts, where emergent processes of joint knowledge construction can take place. Based on this assumption, we inquire into the human and into the AI parts of communication and text production. For our line of argument, we borrow from research on individual and collaborative writing, group cognition, and the co-evolution of cognitive and social systems. We outline future CSCL research paths that might take the human-AI co-construction of knowledge into account in terms of terminology, theory, and methodology.

Working solutions to problems are not definitive end points. As a result, code that is technically correct can still be treated as needing revising – a practice in computer programming known as refactoring. We document how late elementary to middle school students and their undergraduate instructors weigh the possibility of refactoring working code in an informal summer computer science workshop. We examined a 20-min stretch of classroom activity in which multiple coding approaches were explicitly evaluated as alternative routes to the same code output. Our theoretical framework draws on the stance triangle, amplifying and attenuating inequity, and an extension of sociomathematical norms. Using the method of interaction analysis, we transcribed and analyzed stretches of talk, gesture, and action during whole class dicourse and small group interactions involving 4–6 students. We investigated how instructors and students introduced, characterized, applied, and contested sociocomputational norms through stancetaking in classroom discourse, which shaped whose voices contributed to the discussion and whose ideas were treated as impactful and praiseworthy in the classroom. Because it is within these discourse spaces that instructors and students interpret and reinterpret sociocomputational norms about what is valued in programming approaches, educational researchers and teachers might attend to these conversation dynamics as one route to fostering more supportive and inclusive learning spaces.

Peer feedback is known to have positive effects on knowledge improvement in a collaborative learning environment. Attributed to technology affordances, class-wide peer feedback could be garnered at a wider range in the networked learning environment. However, more empirical studies are needed to explore further the effects of type and depth of feedback on knowledge improvement. In this mixed method research, 38 students underwent a computer-supported collaborative learning (CSCL) lesson in an authentic classroom environment. Both quantitative and qualitative analyses were conducted on the collected data. Pre- and post-test comparison results showed that students’ conceptual knowledge on adaptations improved significantly after the CSCL lesson. Qualitative analysis was conducted to examine how the knowledge improved before and after the peer feedback process. The results showed that the class-wide intergroup peer feedback supported learners, with improvement to the quality of their conceptual knowledge when cognitive capacity had reached its maximum at the group level. The peer comments that seek further clarity and suggestions prompted deeper conceptual understanding, leading to knowledge improvement. However, such types of feedback were cognitively more demanding to process. The implications of the effects of type of peer feedback on knowledge improvement and the practical implications of the findings for authentic classroom environments are discussed.

Despite their potential to deliver a high-quality learning experience, massive open online courses (MOOCs) pose several issues, such as high dropout rates, difficulties in collaboration between students, low teaching involvement, and limited teacher–student interaction. Most of these issues can be attributed to the large number, diversity, and variation in self-regulated learning (SRL) skills of participants in MOOCs. Many instructional designers try to overcome these issues by incorporating collaborative activities. Others try to scaffold students’ SRL levels by making SRL-focused interventions. However, limited research combines the study of SRL-focused interventions with students’ engagement in collaborative activities, course retention, and learning outcomes of MOOC environments. We deployed a programming-oriented MOOC in which we incorporated chat-based collaborative activities, supported by a learning analytics dashboard. Students were asked to complete SRL-focused questionnaires at the beginning and the end of the course. Based on their score, we calculated an average score that forms their SRL level, creating three groups: (a) control, (b) general intervention, and (c) personalized intervention in which we provided personalized interventions. We compared the students’ learning outcomes, participation in collaborative activities, and retention in the MOOC. These comparisons provided evidence regarding the positive impact of different intervention modes on students’ engagement in collaborative activities and their learning outcomes, with respect to their various SRL profiles. Students allocated to the general and personalized intervention groups displayed increased participation in the collaborative activities and learning outcomes, as compared to students assigned to the control group. We also documented that the SRL interventions positively affected students’ course retention.

Online peer feedback has proven to be practically useful for instructors and to be useful for learning, especially for the feedback provider. Because students can vary widely in skill level, some research has explored matching reviewer and author by performance level. However, past research on the impacts of reviewer matching has found little effect but used a simple binary high–low approach, which may mask the relative benefits of performance matching. In the current study, we leveraged a large dataset involving three large biology courses implementing multiple assignments with online peer feedback. This large dataset enabled dividing students into four levels of relative task performance to tease apart the relative contributions of providing and receiving feedback within the 16 different author–reviewer performance pairings. The results reveal that changes in task performance over assignments attributable to reviewing experiences vary by these finer-grained prior performance distinctions. In particular, providing to students at the same performance level appears to be beneficial, and receiving feedback from students at the same level is helpful except for very low-performing students. A simulation was used to examine the combined effects of receiving and providing under different algorithms for assigning reviewers to documents. The simulations suggest a matching algorithm will produce overall better outcomes than a random assignment algorithm for students at each of the four performance levels.

This study examined students’ understanding of, and reflective inquiry into discourse, specifically their epistemic discourse understanding and meta-discourse, and investigated their roles and relationships in fostering productive inquiry in knowledge building. The participants comprised two classes of ninth grade visual arts students inquiring into art and design. The experimental class (n = 31) engaged in knowledge building using Knowledge Forum® (KF) enriched by meta-discourse involving reflective inquiry and classroom discussion about their discourse. The comparison class (n = 32) similarly worked on KF but using regular classroom discussion. Quantitative analysis indicated that the experimental group students, who engaged in meta-discourse, showed a deeper epistemic discourse understanding and domain knowledge than the comparison students, and that epistemic discourse understanding was associated with productive KF inquiry. Qualitative analysis of the classroom meta-discourse showed that metacognitive reflection, principle-based inquiry, and idea development (i.e., meta-epistemic reflection, meta-epistemic principles, and meta-epistemic theory) support epistemic understanding and productive inquiry. We also discuss the implications of using meta-discourse to enhance epistemic discourse understanding and productive inquiry for knowledge building and computer-supported collaborative learning.

Understanding how individuals collaborate with others is a complex undertaking, because collaborative problem-solving (CPS) is an interactive and dynamic process. We attempt to identify distinct collaborative problem-solver profiles of Chinese 15-year-old students on a computer-based CPS task using process data from the 2015 Program for International Student Assessment (PISA, N = 1,677), and further to examine how these profiles may relate to student demographics (i.e., gender, socioeconomic status) and motivational characteristics (i.e., achieving motivation, attitudes toward collaboration), as well as CPS performance. The process indicators we used include time-on-task, actions-on-task, and three specific CPS process skills (i.e., establish and maintain shared understanding, take appropriate action to solve the problem, establish and maintain team organization). The results of latent profile analysis indicate four collaborative problem-solver profiles: Disengaged, Struggling, Adaptive, and Excellent. Gender, socioeconomic status, attitudes toward collaboration and CPS performance are shown to be significantly associated with profile membership, yet achieving motivation was not a significant predictor. These findings may contribute to better understanding of the way students interact with computer-based CPS tasks and inform educators of individualized and adaptive instructions to support student collaborative problem-solving.