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This meta-analysis synthesizes research findings on the effects of computer-supported collaborative learning (CSCL) based on its three main elements: (1) the collaboration per se, (2) the use of computers, and (3) the use of extra learning environments or tools, or supporting strategies in CSCL. In this analysis, 425 empirical studies published between 2000 and 2016 were extracted and coded, and these generated the following findings. First, the collaboration had significant positive effects on knowledge gain (ES [effect size] = 0.42), skill acquisition (ES = 0.64), and student perceptions (ES = 0.38) in computer-based learning conditions. Second, computer use led to positive effects on knowledge gain (ES = 0.45), skill acquisition (ES = 0.53), student perceptions (ES = 0.51), group task performance (ES = 0.89), and social interaction (ES = 0.57) in collaborative learning contexts. Third, the use of extra learning environments or tools produced a medium effectfor knowledge gain (ES = 0.55), and supporting strategies resulted in an ES of 0.38 for knowledge gain. Several study features were analyzed as potential moderators.

Cognitive load theory has traditionally been associated with individual learning. Based on evolutionary educational psychology and our knowledge of human cognition, particularly the relations between working memory and long-term memory, the theory has been used to generate a variety of instructional effects. Though these instructional effects also influence the efficiency and effectiveness of collaborative learning, be it computer supported or face-to-face, they are often not considered either when designing collaborative learning situations/environments or researching collaborative learning. One reason for this omission is that cognitive load theory has only sporadically concerned itself with certain particulars of collaborative learning such as the concept of a collective working memory when collaborating along with issues associated with transactive activities and their concomitant costs which are inherent to collaboration. We illustrate how and why cognitive load theory, by adding these concepts, can throw light on collaborative learning and generate principles specific to the design and study of collaborative learning.

Scripts for computer-supported collaborative learning (CSCL) offer socio-cognitive scaffolding for learners to engage in collaborative activities that are considered beneficial for learning. Yet, CSCL scripts are often criticized for hampering naturally emerging collaboration. Research on the effectiveness of CSCL scripts has shown divergent results. This article reports a meta-analysis about the effects of CSCL scripts on domain-specific knowledge and collaboration skills. Results indicate that CSCL scripts as a kind of socio-cognitive scaffolding can enhance learning outcomes substantially. Learning with CSCL scripts leads to a small positive effect on domain-specific knowledge (d=0.20) and a large positive effect on collaboration skills (d=0.95) compared to unstructured CSCL. Further analyses reveal that CSCL scripts are particularly effective for domain-specific learning when they prompt transactive activities (i.e., activities in which a learner's reasoning builds on the contribution of a learning partner) and when they are combined with additional content-specific scaffolding (worked examples, concept maps, etc.). Future research on CSCL scripts should include measures of learners' internal scripts (i.e., prior collaboration skills) and the transactivity of the actual learning process.

Research on computer-supported collaborative learning (CSCL) has traditionally investigated how student-, group-, task-, and technological characteristics affect the processes and outcomes of collaboration. On the other hand, cognitive load theory has traditionally been used to study individual learning processes and to investigate instructional effects that are present during individual learning (e.g., expertise reversal effect). In this contribution we will argue that cognitive load theory can be applied to CSCL. By incorporating concepts such as collective working memory (i.e., individuals share the burden of information processing), mutual cognitive interdependence (i.e., individuals learn about each other’s expertise and become dependent on their partners’ expertise), and transaction costs (i.e., the burden placed on individuals working memory capacity when communicating and coordinating collaborative activities), collaborative cognitive load theory (CCLT) can be used to formulate testable hypotheses for pressing issues in CSCL research. The aim of this paper is to develop a research agenda to guide future CSCL research from a CCLT perspective. We highlight how variables associated with student-, group-, task-, and technological characteristics may be investigated using CCLT. We also address important steps CSCL research needs to make with respect to the measurement of variables and the methodologies used to analyze data.

For effective computer supported collaborative learning (CSCL), socially shared regulation of learning (SSRL) is necessary. To this end, this article extends the idea first posited by Järvelä and Hadwin (Educ Psychol 48(1):25–39, 2013) that successful collaboration in CSCL contexts requires targeted support for promoting individual self-regulatory skills and strategies, peer support, facilitation of self-regulatory competence within the group, and SSRL. These (meta)cognitive, social, motivational, and emotional aspects related to being/becoming aware of how one learns alone and with others are for the most part neglected in traditional CSCL support. Based upon a review of theoretical and empirical studies on the potential of and challenges to collaboration, three design principles for supporting SSRL are introduced: (1) increasing learner awareness of their own and others' learning processes, (2) supporting externalization of one's own and others' learning process and helping to share and interact, and (3) prompting acquisition and activation of regulatory processes. Finally, an illustrative example is presented for how these principles are applied in a technological tool for supporting SSRL.

While a number of studies have considered that metacognition is related to processes at an individual level, the role of metacognition during collaborative learning activities remains unclear. Metacognition has been studied mainly as a process of the individual, neglecting the relevance of group regulated behavior during cooperative activities and how group members perceive their skills and reflect on group potentialities. The current study presents the construction and validation of a 20-item quantitative scale for measuring the metacognition of groups based on their knowledge of cognition, planning, monitoring and evaluating. The tool was presented to 362 university students participating in online collaborative activities. The validity and reliability of the scale were verified calculating descriptive statistics, the KMO and Bartlett tests, exploratory factor analysis, Cronbach's alpha, a confirmatory factor analysis and multi-group invariance testing. The findings showed that the instrument is sufficiently valid and reliable. To demonstrate its utility, the scale was used to observe differences in the processes among students attending several courses. Trainee teachers of primary school reported a higher metacognitive level than students in psychology, for example. The findings indicate that metacognition should also be considered in a group dimension rather than only as a reflection of individual behavior, and it should be a relevant construct for understanding online collaborative processes. Ways in which the scale could be applied to improve CSCL and further research for assessing the correlation between metacognition and other constructs are also discussed.

Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students’ discourses, behaviors, and socio-emotions, it is of critical importance to examine their collaborative patterns from a holistic, multimodal, dynamic perspective. But there is a lack of research investigating the collaborative patterns generated by the multimodality. This research applied multimodal learning analytics (MMLA) to collect 19 undergraduate student pairs’ multimodal process and products data to examine different collaborative patterns based on the quantitative, structural, and transitional characteristics. The results revealed four collaborative patterns (i.e., a consensus-achieved pattern, an argumentation-driven pattern, an individual-oriented pattern, and a trial-and-error pattern), associated with different levels of process and summative performances. Theoretical, pedagogical, and analytical implications were provided to guide the future research and practice.

Learning engagement has gained increasing attention in the field of education. Previous studies have adopted conventional methods to analyze learning engagement, but these methods cannot provide timely feedback for learners. This study analyzed automated group learning engagement via deep neural network models in a computer-supported collaborative learning (CSCL) context. A quasi-experimental research design was implemented to examine the effects of the automated group learning engagement analysis and feedback approach on collaborative knowledge building, group performance, socially shared regulation, and cognitive load. In total, 120 college students participated in this study; they were assigned to 20 experimental groups and 20 control groups of three students each. The students in the experimental groups adopted the automated group learning engagement analysis and feedback approach, whereas those in the control groups used the traditional online collaborative learning approach. Both quantitative and qualitative data were collected and analyzed in depth. The results indicated significant differences in group learning engagement, group performance, collaborative knowledge building, and socially shared regulation between the experimental and control groups. The proposed approach did not increase the cognitive load for the experimental groups. The implications of the findings can potentially contribute to improving group learning engagement and group performance in CSCL.

Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing methods attempt to generate the missing data with multiple available modalities. However, the modality differences in tissue contrast and lesion appearance become an obstacle to making a precise estimation. To address this issue, we propose an autoencoder-driven multimodal collaborative learning framework for medical image synthesis. The proposed approach takes an autoencoder to comprehensively supervise the synthesis network using the self-representation of target modality, which provides target-modality-specific prior to guide multimodal image fusion. Furthermore, we endow the autoencoder with adversarial learning capabilities by converting its encoder into a pixel-sensitive discriminator capable of both reconstruction and discrimination. To this end, the generative model is completely supervised by the autoencoder. Considering the efficiency of multimodal generation, we also introduce a modality mask vector as the target modality label to guide the synthesis direction, empowering our method to estimate any missing modality with a single model. Extensive experiments on multiple medical image datasets demonstrate the significant generalization capability as well as the superior synthetic quality of the proposed method, compared with other competing methods. The source code will be available: https://github.com/bcaosudo/AE-GAN.

While there are many studies on students’ collaborative learning at the small group level, pedagogies and strategies for supporting students’ collaborative learning at the class level are underexplored. This study proposes a pedagogical model named the Spiral Model of Collaborative Knowledge Improvement (SMCKI) to inform the design and implementation of multi-layered collaborative learning activities in a networked class where there are many groups of students working collaboratively. Starting with a phase of individual ideation, the pedagogical model scaffolds students to go through five phases of intra-group and inter-group knowledge improvement and refinement, with the goal of supporting the advancement of their individual and collective knowledge. An exploratory case study is presented to illustrate how this model was used in a pre-service teachers’ technology-enhanced learning (TEL) activity design lesson in a Computer-Supported Collaborative Learning (CSCL) environment. The results show that the participants significantly improved the quality of TEL design throughout the five phases of SMCKI. The implications of the findings on designing and implementing CSCL activities in authentic class environments are discussed.