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\"Seeking to broaden current understanding of collective creation, this edited volume situates its contemporary practice in the tradition which emerged at the beginning of the twentieth century. The contributors trace the aesthetic, social, and political shifts that have marked this mode of theatre making, from mid-century manifestations as a theatrical expression of the New Left to present-day devising practices. With emphasis on Russia, Europe, and North America, Collective Creation in Contemporary Performance examines collective and devised theatre practices internationally and demonstrates the prevalence, breadth, and significance of collective creation in modern times\"-- Provided by publisher.

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.

[Display omitted] •AI advances from mechanical to thinking to feeling, changing how AI should be used.•AI and human intelligence (HI) complement best as collaborative teams.•Lower-level AI augments higher-level HI.•AI first augments and then replaces HI at a given intelligence level.•Move HI to a higher intelligence level when AI automates the lower level. We develop a conceptual framework for collaborative artificial intelligence (AI) in marketing, providing systematic guidance for how human marketers and consumers can team up with AI, which has profound implications for retailing, which is the interface between marketers and consumers. Drawing from the multiple intelligences view that AI advances from mechanical, to thinking, to feeling intelligence (based on how difficult for AI to mimic human intelligences), the framework posits that collaboration between AI and HI (human marketers and consumers) can be achieved by 1) recognizing the respective strengths of AI and HI, 2) having lower-level AI augmenting higher-level HI, and 3) moving HI to a higher intelligence level when AI automates the lower level. Implications for marketers, consumers, and researchers are derived. Marketers should optimize the mix and timing of AI-HI marketing team, consumers should understand the complementarity between AI and HI strengths for informed consumption decisions, and researchers can investigate innovative approaches to and boundary conditions of collaborative intelligence.

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.

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.

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.

Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance of examining the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical research examining the adaptive and temporal characteristics of CPS, which may have led to an oversimplified representation of the real complexity of the CPS process. To expand our understanding of the nature of CPS in online interaction settings, the present research collected multimodal process and performance data (i.e., speech, computer screen recordings, concept map data) and proposed a three-layered analytical framework that integrated AI algorithms with learning analytics to analyze the regularity of groups’ collaboration patterns. The results surfaced three types of collaborative patterns in groups, namely the behaviour-oriented collaborative pattern (Type 1) associated with medium-level performance, the communication-behaviour-synergistic collaborative pattern (Type 2) associated with high-level performance, and the communication-oriented collaborative pattern (Type 3) associated with low-level performance. This research further highlighted the multimodal, dynamic, and synergistic characteristics of groups’ collaborative patterns to explain the emergence of an adaptive, self-organizing system during the CPS process. According to the empirical research results, theoretical, pedagogical, and analytical implications were discussed to guide the future research and practice of CPS.

In today’s digital society, computer-supported collaborative learning (CSCL) and collaborative problem solving (CPS) have received increasing attention. CPS studies have often emphasized outcomes such as skill levels of CPS, whereas the action transitions in the paths to solve the problems related to these outcomes have been scarcely studied. The patterns within action transitions are able to capture the mutual influence of actions conducted by pairs and demonstrate the productivity of students’ CPS. The purpose of the study presented in this paper is to examine Finnish sixth graders’ (N = 166) patterns of action transitions during CPS in a computer-based assessment environment in which the students worked in pairs. We also investigated the relation between patterns of action transitions and students’ social and cognitive skill levels related to CPS. The actions in the sequential processes of computer-based CPS tasks included using a mouse to drag objects and typing texts in chat windows. Applying social network analysis to the log file data generated from the assessment environment, we created transition networks using weighted directed networks (nodes for those actions conducted by paired students and directed links for the transitions between two actions when the first action is followed by the second action in sequence). To represent various patterns of action transitions in each transition network, we calculated the numbers of nodes (numbers of actions conducted), density (average frequency of transitions among actions), degree centralization (the dispersion of attempts given to different actions), reciprocity (the extent to which pairs revisit the previous one action immediately), and numbers of triadic patterns (numbers of different repeating formats within three actions). The results showed that pairs having at least one member with high social and high cognitive CPS skills conducted more actions and demonstrated a higher average frequency of action transitions with a higher tendency to conduct actions for different number of times, implying that they attempted more paths to solve the problem than the other pairs. This could be interpreted as the pairs having at least one student with high social and high cognitive CPS skills exhibiting more productive CPS than the other pairs. However, we did not find a significant difference across the pairs in terms of alternating sequences of two or three actions. Investigating the patterns of action transitions of the dyads in this study deepens our understanding of the mutual influence between the CPS actions occurring within dyads. Regarding pedagogical implication, our results offer empirical evidence recommending greater awareness of the students’ social and cognitive capacities in CPS when assigning them into pairs for computer-based CPS tasks. Further, this study contributes to the methodological development of process-oriented research in CSCL by integrating an analysis of action transition patterns with a skill-based assessment of CPS.

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.

Recent technological advances in online and mobile communications have enabled collaborative consumption or product sharing among consumers on a massive scale. Collaborative consumption has emerged as a major trend as the global economic recession and social concerns about consumption sustainability lead consumers and society as a whole to explore more efficient use of resources and products. We develop an analytical framework to examine the strategic and economic impact of product sharing among consumers. A consumer who purchased a firm’s product can derive different usage values across different usage periods. In a period with low self-use value, the consumer may generate some income by renting out her purchased product through a third-party sharing platform as long as the rental fee net of transaction costs exceeds her own self-use value. Our analysis shows that transaction costs in the sharing market have a nonmonotonic effect on the firm’s profits, consumer surplus, and social welfare. We find that when the firm strategically chooses its retail price, consumers’ sharing of products with high marginal costs is a win-win situation for the firm and the consumers, whereas their sharing of products with low marginal costs can be a lose-lose situation. Furthermore, in the presence of the sharing market, the firm will find it optimal to strategically increase its quality, leading to higher profits but lower consumer surplus. This paper was accepted by J. Miguel Villas-Boas, marketing .