Notes on Computer Supported Collaborative Learning Handbook 2021

The field of computer supported collaborative learning (CSCL) has been around for decades and is a major component of remote learning. It can also be seen as part of Critical Thinking since instructors generally want students working in discussion groups to be able to think, speak, and write clearly.

Face-to-face discussion groups are very important, and can either be lead by teachers or students. When students lead their own groups, it is sometimes called team learning.

Computer supported collaborative learning is often associated with remote learning since students often use computers to communicate at a distance. They can also communicate either synchronously (as with Zoom) or asynchronously.

The handbook on CSCL is a major resource on that field of inquiry. Here are some of its features:

  • Each article has an Abstract, Keywords, and References.
  • See Abstract and Keywords below.
  • See References at link.
  • Each article can be purchased separately. See price at link.
  • Hardcover new book price on 2022-01-16: USD $220

International Handbook of Computer-Supported Collaborative Learning
Ulrike Cress, Carolyn Rosé, Alyssa Friend Wise, Jun Oshima (Editors)
1 January 2021

Key Features of the Book

  • Provides a comprehensive overview of the diverse aspects of the field of computer-supported collaborative learning useful for newcomers or experts.
  • Serves as a ready reference for foundational concepts, methods, and approaches in the field.
  • Integrates myriad research perspectives, including but not limited to learning sciences, education, computer science, computational linguistics, psychology, and sociology.
  • Speaks to a wide, growing, and international community of CSCL researchers.

Book Series

Part of the Computer-Supported Collaborative Learning Series book series (CULS, volume 19)

Introduction to Book

CSCL has in the past 15 years (and often in conjunction with Springer) grown into a thriving and active community. Yet, lacking is a comprehensive CSCL handbook that displays the range of research being done in this area. This handbook will provide an overview of the diverse aspects of the field, allowing newcomers to develop a sense of the entirety of CSCL research and for existing community members to become more deeply aware of work outside their direct area. The handbook will also serve as a ready reference for foundational concepts, methods, and approaches in the field. The chapters are written in such a way that each of them can be used in a stand-alone fashion while also serving as introductory readings in relevant study courses or in teacher education. While some CSCL-relevant topics are addressed in the International Handbook of the Learning Sciences and the International Handbook of Collaborative Learning, these books do not aim to present an integrated and comprehensive view of CSCL. The International Handbook of Computer- Supported Collaborative Learning covers all relevant topics in CSCL, particularly recent developments in the field, such as the rise of computational approaches and learning analytics.

Keywords for Book

Foundations of computer-supported collaborative learning (CSCL)
Contexts of computer-supported collaborative learning (CSCL)
Methods of computer-supported collaborative learning (CSCL)
Outcomes of computer-supported collaborative learning (CSCL)
Group formation
Knowledge construction
Knowledge building
Object-oriented collaboration
Group cognition
Socially-shared regulation
Design of hard and soft technologies
Technology effects and affordances
Interactive and mobile surfaces
Games and virtual worlds
Immersive environments
Robots and agents
Learning analytics
Qualitative & quantitative approaches
Multivocality approach

About the editors

Ulrike Cress is director of the Leibniz-Institut für Wissensmedien (Knowledge Media Research Center) and professor at the University of Tübingen. She studied psychology in Tuebingen, Germany, did a dissertation about self-regulated learning and got a venia legendi for psychology with a work on the information-exchange dilemma. With her lab “knowledge construction” she is doing research on learning with digital media in formal settings as well as in informal settings. She is especially interested in mass collaboration, and much of her research aims to further develop the Co-Evolution Model of Individual Learning and Collaborative Knowledge Building, which she presented in 2008. She published more than 150 articles in peer-reviewed journals, and about 100 book chapters and contributions to conference proceedings. Since 2015 she is one of the four executive editors of the International Journal of Computer-supported Collaborative Learning.

Carolyn Rosé is a professor in the School of Computer Science at Carnegie Mellon University. Her research program is focused on better understanding the social and pragmatic nature of conversation and using this understanding to build computational systems that can improve the efficacy of conversation between people, and between people and computers. She grounds her research in the fields of language technologies and human-computer interaction and works closely with the Language Technologies Institute and the Human-Computer Interaction Institute, as well as serving as the director if the TELEDIA lab. Her group’s highly interdisciplinary work, published in over 250 peer reviewed publications, is represented in the top venues in 5 fields: namely, Language Technologies, Learning Sciences, Cognitive Science, Educational Technology, and Human-Computer Interaction, with awards or award nominations in 3 of these fields.

Alyssa Wise is Associate Professor of Learning Sciences and Educational Technology in the Steinhardt School of Culture, Education, and Human Development at New York University and the Director of NYU-LEARN, NYU’s pioneering university-wide Learning Analytics Research Network. She holds a Ph.D. in the Learning Sciences and M.S. in Instructional Systems Technology, both from Indiana University, and a B. S. in Chemistry from Yale University. Dr. Wise’s research focuses on the design of learning analytics systems that are theoretically grounded, computationally robust, and pedagogically useful for informing teaching and learning. Her most recent work has focused on analytics of collaboration, reflection, and math learning and the study of how people take up analytics as part of their educational practices. In addition to her numerous research contributions, Dr. Wise serves as Co-Editor-in-Chief of the Journal of Learning Analytics and on the editorial boards of the Journal of the Learning Sciences and the International Journal of Computer-Supported Collaborative Learning

Jun Oshima is a professor at the Graduate School of Integrated Science and Technology at Shizuoka University, Japan. He holds Ph.D. in applied cognitive science from the University of Toronto, M.A. from Hiroshima University, and B.A. from Fukuoka University of Education in educational psychology. Dr. Oshima’s research interest includes the instructional designs for learning as knowledge-creation from elementary schools through post-secondary institutions. Besides the design-based research, He has been conducting the development and application of socio-semantic network analysis of discourse to evaluate collective knowledge advancement in the knowledge-creation practice in recent years. He serves on the editorial boards of The Journal of the Learning Sciences and the International Journal of Computer-Supported Collaborative Learning.

Table of Contents

Front Matter
Pages i-xiii


Front Matter
Pages 1-1

Foundations, Processes, Technologies, and Methods: An Overview of CSCL Through Its Handbook
Ulrike Cress, Jun Oshima, Carolyn Rosé, Alyssa Friend Wise
Pages 3-22

Computer-supported collaborative learning (CSCL) has been a topic of research for more than 30 years. This first international handbook provides an overview of CSCL research, describing the history, development and state-of-the-art for key areas of work in the field. It approaches the topic from four different perspectives that form the sections of the handbook: theoretical foundations, collaborative processes, relevant technologies, and common research methods. This introduction chapter provides an overview of the entire handbook by briefly summarizing the topic and content of each of the 34 chapters as well as drawing out themes and connections among them.

Computer-supported collaborative learning Collaborative processes Computer-supported collaboration Collaborative learning Collaboration

Theories of CSCL
Gerry Stahl, Kai Hakkarainen
Pages 23-43

This chapter examines collaborative learning as cognition at the small-group unit of analysis, and highlights theoretical questions concerning interrelationships among individual, collective, and cultural cognition. CSCL is a theory- and research-based pedagogical vision of what collaborative learning could be like, thanks to innovative computational supports and new ways of thinking about learning. Theories of CSCL are shaped by rapidly evolving digital technologies, pedagogical practices, and research methods. Relevant theories can be categorized as: subjective (individual cognition and learning), intersubjective (interactional meaning making), and inter-objective (networks of learners, tools, artifacts, and practices). Theoretical insights suggest ways of enhancing, supporting, and analyzing cognition and learning by individuals, groups, and communities. The emerging ecology of socio-digital participation—involving students’ daily use of computers, mobile devices, social media, and the Internet—requires extending and synthesizing CSCL theories to conceptualize connected learning at multiple levels.

Subjective Inter-subjective Inter-objective Socio-cognitive Socio-cultural Ethnomethodology Dialogism Knowledge building Activity theory Actor network Group cognition Group practice

A Conceptual Stance on CSCL History
Sten Ludvigsen, Kristine Lund, Jun Oshima
Pages 45-63

CSCL is focused on the interdependence of social interaction and computational artifacts. A computational artifact mediates participants’ sensemaking in the collaboration. The sensemaking is dependent on what the participants do together with the computational artifact. The analysis of this mediation unveils core CSCL processes. CSCL draws on foundations in social, learning, and computer sciences. In this historical chapter, we focus on epistemic issues in CSCL. The focus on methodological stances and computational artifacts varies between the different strands of research, whereas the epistemological stances were established early on in the history of CSCL and have remained stable. Conceptualizing CSCL as the interdependency between collaborating participants and computational artifacts requires the definition of a unit of analysis that can explain and help us understand what and how people learn in collaboration. The way concepts in CSCL studies are operationalized signals the epistemological stance that authors make use of.

Collabortive learning Computational artifacts Epistemology Methodology Sensemaking Learning sciences Computer sciences

An Overview of CSCL Methods
Cindy E. Hmelo-Silver, Heisawn Jeong
Pages 65-83

CSCL as a field incorporates diverse methodological practices. This chapter provides an overview of research methods and practices in CSCL. Research methods are designed to answer those questions central to CSCL, whereas the practices refer to how these methods are used in practice. The chapter considers the diversity of methodological practices that are used to address the questions that CSCL researchers ask. In particular, research designs, settings, data sources, and analysis methods are reviewed using a literature corpus of CSCL research in STEM fields from 2005 to 2014. The results of this review show the range of practices used in CSCL research and the common practice of mixing methods. Finally, future trends related to visualization, automated analysis, and multimodal data are considered. These future trends address the complexity and diversity of CSCL environments as well as the challenges in analyzing the vast amount of data in seeking to support and understand collaborative sensemaking.

Research methods Research design Methodological practices

Conceptualizing Context in CSCL: Cognitive and Sociocultural Perspectives
Camillia Matuk, Kayla DesPortes, Christopher Hoadley
Pages 85-101

Context is a critical consideration in CSCL research and design, yet difficult to delineate. Its definition can encompass aspects of the environment, the learners, the technology, and their histories and cultures. Depending on researchers’ theoretical perspectives and the focus of their study, different aspects of context are forefronted in the data collection and analysis, while others are given less importance. In this chapter, we offer a framework that conceptualizes context in terms of focal, immediate, and peripheral layers surrounding the object of study. We describe how the aspects contained within each layer of context differ depending on one’s theoretical orientation. To illustrate, we offer contrasting examples of CSCL research that approach context from a cognitive perspective and a sociocultural perspective. We end by outlining several areas for future research and highlight the importance of technological advances to keep pace with the theoretical conceptions of context in order to support the design of responsive CSCL environments. Ultimately, we argue that a full understanding of context leads to more robust and ecologically sound CSCL research and design.

Cognitive theory Context Computer-supported collaborative learning Sociocultural theory Technology

Interrogating the Role of CSCL in Diversity, Equity, and Inclusion
Kimberley Gomez, Louis M. Gomez, Marcelo Worsley
Pages 103-119

The underlying aim of this chapter is to contribute to efforts to build and organize the design landscape and vocabulary for conversations about diversity, equity, and inclusion (DEI) in CSCL. Anchoring our discussion is the position that DEI can only really be understood and achieved at scale. We have limited our scope to include the consideration of three critical issues—language, differentiation, and identity—that we believe serve to, however unintentionally, restrict or promote DEI in CSCL, perennial problems that often surface in complex software systems, which may prevent broad-based utility in applications, and how issues of DEI surface themselves in these designed tools and applications. We center this discussion in a few common CSCL applications: contexts like MOOCs, virtual high schools, and networked-based multiplayer games. We highlight three core DEI challenges present in the use of CSCL environments: language, differentiation, and identity as focal components that designers should be aware of as applications move to scale.

Equity Diversity Inclusion CSCL Language Differentiation Identity

Sustainability and Scalability of CSCL Innovations
Nancy Law, Jianwei Zhang, Kylie Peppler
Pages 121-141

CSCL innovations involve dynamic changes taking place at multiple levels within the complex educational ecosystem. Scaling of CSCL innovations needs to pay simultaneous attention to changes along several dimensions, including depth of change, sustainability, spread, and shifts of ownership, as well as evolution of the innovation over time. General models for scaling innovations do not take account of the role that technology may play. This chapter examines the sustainability and scalability of CSCL innovations including the role of technology in fostering sustainable and scalable innovation. We review a range of CSCL innovations that span in- and out-of-school settings to synthesize technology-enabled strategies that address scalability challenges at the classroom and education ecosystem levels. A set of design principles is identified to guide future research and practice to transform education through CSCL innovations.

Sustainability Scalability Scaling CSCL innovations Design principles for scalability Architecture for learning Innovation network Multilevel aligned learning

Collaborative Processes

Front Matter
Pages 143-143

Communities and Participation
Yotam Hod, Stephanie D. Teasley
Pages 145-161


This chapter explores the closely related concepts of “communities” and “participation” as they relate to the field of computer-supported collaborative learning (CSCL). We describe how these terms have become foundational to work in CSCL, both in terms of theory development and for understanding how to design effective CSCL communities. In our review of the state-of-the-art, we highlight how rapid technological developments have created new opportunities for more meaningful and advanced forms of participation in communities. We conclude with our challenge to the future direction of the field, describing how notions of participation in communities are currently being renegotiated within the conception of the “spatial turn.” In the era of “big data” that includes exhaustive records of educational data in formal and informal spaces, we caution our CSCL colleagues to remember our theoretical roots and humanistic values as we move forward and continue to shape the future of CSCL.

Communities CSCL Participation Spatial turn Sociocultural

Collaborative Learning at Scale
Bodong Chen, Stian Håklev, Carolyn Penstein Rosé
Pages 163-181


The CSCL community has traditionally focused on collaborative learning in small groups or communities. Given the rise of mass collaboration and learning at scale, the community is facing unprecedented opportunity to expand its views to advance collaborative learning at scale. In this chapter, we first explicate the history and development of collaborative learning at scale and contend that both learning and collaboration need to be reconceptualized for the nascent context. We propose a framework that considers scale as either a problem to be mitigated or an asset to be harnessed, and then review pedagogical and technological innovations representing these two approaches. We conclude by discussing key tensions and challenges facing collaborative learning at scale.

Collaborative learning Learning at scale Mass collaboration Social media Massive open online courses

Argumentation and Knowledge Construction
Joachim Kimmerle, Frank Fischer, Ulrike Cress
Pages 183-198


We examine the role of argumentation in knowledge construction during computer-supported collaborative learning (CSCL). We describe the history and development of argumentation research from early precursors to the examination of argumentation in everyday life. We also present the development of tools and methods that have been applied for the empirical investigation of argumentation in CSCL. In presenting the state of the art of research on argumentation and knowledge construction, we include studies of reflective interactions and the analysis of “uptake events” in conversation. We also analyze argumentative knowledge construction in online contexts and science education. We discuss the debate on the extent to which argumentation supports the development of domain-specific or domain-general knowledge. In concluding, we point to some potential future development in research on argumentation and knowledge construction, such as the consideration of additional influencing factors like social context or emotions.

Argumentation Knowledge construction Computer-supported collaborative learning Reflective interactions Co-evolution model

Analysis of Group Practices
Richard Medina, Gerry Stahl
Pages 199-218

This chapter introduces an approach to CSCL research driven by the analysis of data displaying how groups adopt, adapt, and master new collaborative knowledge-building practices. The analysis of group practices can provide unique insight into the accomplishments of teams of students in CSCL settings. It conceptualizes a theory of learning with the group as the unit of analysis in terms of the acquisition of group practices. CSCL pedagogy can then be oriented toward orchestrating the adoption of targeted group practices, supported by CSCL technology.

Ethnomethodology Group practice Group cognition Interaction Orchestration Representational practice Segmentation Sequential analysis Social practice Unit of analysis Uptake

Stefan Trausan-Matu, Rupert Wegerif, Louis Major
Pages 219-239

Dialogism offers a theoretical framework for understanding computer-supported collaborative learning (CSCL). This framework begins with Mikhail Bakhtin’s claim that meaning making requires the interanimation of more than one ‘voice’ as in polyphonic music. Dialogism offers an approach that leads to understanding through the juxtaposition of multiple perspectives. As well as having implications for how we research CSCL, dialogism also has implications for how we conceptualise the goal of CSCL, suggesting the aim of deepening and widening dialogic space. This chapter reviews research within a dialogic CSCL frame, offers a cutting-edge example and presents predictions and suggestions for the future of dialogism within CSCL.

Dialogism Dialogic education Dialogue Dialogic CSCL Educational technology Edtech Digital technology Polyphonic model

Trialogical Learning and Object-Oriented Collaboration
Sami Paavola, Kai Hakkarainen
Pages 241-259

This chapter delineates different approaches to technology-mediated learning that emphasize “object-oriented” collaboration. The chapter introduces, more specifically, trialogical learning, as distinguished from individual knowledge acquisition (“monological”) or from participation in social interaction and meaning making (“dialogical” approaches, see Trausan-Matu, Wegerif, & Major, this volume). We briefly introduce object-oriented collaboration and the trialogical approach where human learning and activity are targeted at jointly developed knowledge artifacts and related knowledge practices. As objects and object-orientedness have become centrally important for understanding collaboration in modern knowledge work, the facilitation of trialogical processes of collaborative learning is crucial in educational contexts. Several approaches focusing on object-oriented collaboration are analyzed, including those that use different terminology. The trialogical approaches appear to form a continuum with dialogical theories and meaning-making traditions often highlighted in CSCL research. Finally, we anticipate future uses of trialogical learning and object-oriented collaboration.

Object-oriented collaboration Trialogical learning Knowledge-creation metaphor Knowledge practices

Knowledge Building: Advancing the State of Community Knowledge
Marlene Scardamalia, Carl Bereiter
Pages 261-279

“Knowledge Building” may be understood as synonymous with “knowledge creation,” as that term is used in organizational science and innovation networks, amplified by a concern with educational benefit and well-being of participants, knowledge for public good, and complex systems conceptions of knowledge creation. Thus, knowledge-building classrooms and networks function in design mode, with “design thinking” as a basic mode of thought. Although one among many constructivist approaches, Knowledge Building is distinguished by an emphasis on advancing the state of community knowledge (comparable to advancing the “state of the art”) and on “epistemic agency”: students’ collective responsibility for idea improvement. Knowledge Forum technology is designed to support knowledge-creating discourse within and between communities and to provide feedback tools that students themselves can use in exercising epistemic agency. Pragmatic epistemological issues are discussed, including prescribed activity structures, external scripts, and conceptual and material artifacts, as these issues relate to self-organization and creative knowledge work.

Knowledge Building community Knowledge Building analytics Knowledge Forum Epistemic agency Conceptual artifacts Activity structures Constructivism

Metacognition in Collaborative Learning
Sanna Järvelä, Jonna Malmberg, Marta Sobocinski, Paul A. Kirschner
Pages 281-294

Research has shown that metacognition plays a role in collaborative learning. We view metacognition as a central process supporting all modes of regulation (i.e., self-regulation, shared regulation, and co-regulation), as it enables learners to control and adapt their cognition, motivation, emotion, and behavior at both the individual and group levels. Our claim is that metacognitive monitoring and regulation of collaborative learning can help reduce the collaborative/transactive costs in collaboration and, therefore, contributes to success in computer-supported collaborative learning (CSCL). In this chapter, we discuss the role of metacognition in CSCL and broaden the discussion to regulation. Since regulation in CSCL has been studied increasingly, we review the current state of the art in that research and conclude how technological and digital tools could be implemented for studying and supporting metacognition and regulation in CSCL.

Metacognition Monitoring Self-regulated learning Collaborative learning CSCL

Group Awareness
Jürgen Buder, Daniel Bodemer, Hiroaki Ogata
Pages 295-313

In CSCL contexts, group awareness refers to the state of being informed about cognitive and social attributes of group members, and being informed about the products that group members create. This chapter traces the historical developments of research on group awareness, and it provides a classification of group awareness tools that distinguishes between two types of group awareness tools (cognitive and social) and five functional levels of group awareness tools (framing, displaying, feedback, problematizing, and scripting). Selected group awareness tools and selected empirical findings are reported, showing how research on group awareness is theoretically motivated. The chapter concludes by discussing future directions both for the development of group awareness tools and for theoretical progress.

CSCL Group awareness Feedback Social comparison Learning analytics

Roles for Structuring Groups for Collaboration
Bram De Wever, Jan-Willem Strijbos
Pages 315-331

The emergence of productive collaboration benefits from support for group interaction. Structuring is a broad way to refer to such support, as part of which roles have become a boundary object in computer-supported collaborative learning. The term structuring is related to—yet distinct from—other approaches to support such as scaffolding, structured interdependence, and scripting. Roles can be conceived as a specific (set of) behavior(s) that can be taken up by an individual within a group. They can be assigned in advance or emerge during group interaction. Roles raise individual group member’s awareness of their own and fellow group member’s responsibilities, and they make an individual’s responsibilities toward the group’s functioning visible for all group members. In future research, pedagogical issues with respect to role design, assignment, and rotation as well as automated detection and visualization of emergent roles, should be addressed.

Roles Structuring CSCL Scripting Scaffolding Regulating


Front Matter
Pages 333-333

Collaboration Scripts: Guiding, Internalizing, and Adapting
Freydis Vogel, Armin Weinberger, Frank Fischer
Pages 335-352


Research and practical experience show that for successful collaborative learning, learners need to be willing and able to engage in particular activities. Learners hardly reach this state when left to collaborate on their own. Thus, collaborative learning may rather be set up with particular instructions to learning together effectively. In this chapter, we introduce the Script Theory of Guidance (SToG) to explain how individual learners obtain, adapt, and use cognitive schemas (i.e., internal scripts) about collaborative learning scenarios. The theory further explains how external collaboration scripts can scaffold collaborative learning processes when learners do not spontaneously activate functional internal scripts for collaborative learning. We report on evidence that shows how scripts may help learners engage in transactive group processes that are conducive to joint knowledge construction. Moving beyond currently used scripts, future scripting may focus on the facilitation of interdisciplinary collaboration and scaffolding of learners’ mutual regulation throughout collaborative learning processes.

Collaboration script Socio-cognitive scaffolding Transactivity Interdisciplinary collaboration

The Roles of Representation in Computer-Supported Collaborative Learning
Shaaron E. Ainsworth, Irene-Angelica Chounta
Pages 353-369


Representational learning is fundamental to CSCL. In this chapter, we consider four distinct roles that representations play as collaborators can: (1) interpret existing representations to create shared knowledge; (2) construct new joint representations based upon negotiation and shared understanding; (3) make representational choices concerning how they or other agents in the collaboration are portrayed; and (4) use representations to express and analyze these activities and their outcomes. We show how this research draws upon multiple theoretical perspectives and attempt to look forward to consider where representational paradigms for CSCL may be going.

Representational learning Multiple representations Collaboration Argumentative diagrams Constructing representations Co-construction Joint construction Self-representation Learning analytics Agents and avatars Simulation

Perspectives on Scales, Contexts, and Directionality of Collaborations in and Around Virtual Worlds and Video Games
Deborah Fields, Yasmin Kafai, Earl Aguilera, Stefan Slater, Justice Walker
Pages 371-388

Interpersonal collaborations play a key role in video games and virtual worlds. Yet historically, research regarding games and virtual worlds in CSCL has focused on the smaller, more localized aspects of collaboration within games and virtual worlds. In this chapter, our goal is to broaden the ways in which we conceptualize the possibilities of CSCL in video games and virtual worlds. We argue for broadly and imaginatively expanding the scales, contexts, and directionality of collaborative learning, considering each area in turn and providing vignettes of research that break traditional bounds in considering what collaboration is, where it takes place, and how to study it in video games and virtual worlds. In the discussion, we turn to next steps for CSCL in addressing issues of scale, access, and methods that capture the richness and diversity of computer-supported collaborative learning in video games and virtual worlds.

Collaborative learning Video games Virtual worlds Games and learning Massive collaboration Affinity spaces

Immersive Environments: Learning in Augmented + Virtual Reality
Noel Enyedy, Susan Yoon
Pages 389-405

We articulate a framework for CSCL researchers to consider when designing activities for learning and participation in immersive environments. We begin with a description of the framework that includes five types of immersive qualities, four of which have been written about in previous frameworks–Sensory, Actional, Narrative, and Social. We believe the fifth quality called Emancipatory immersion, advances the field by providing a sense of purpose for learning and participation that is larger than oneself that can transform identities and conceptions of empowerment and action. We then use the SANSE framework to investigate synergies with these qualities among four genres of immersive environments that CSCL researchers have investigated: (1) Headset VR; (2) Desk-Top Virtual Worlds; (3) Space-Based AR; and (4) Place-Based AR. We intend to contribute to dialogue about the kinds of designs for immersive tools the field can and should be thinking about to improve educational conditions.

Collaborative learning Emancipatory learning Virtual reality Augmented reality Mixed reality

Robots and Agents to Support Collaborative Learning
Sandra Y. Okita, Sherice N. Clarke
Pages 407-424

This chapter examines how social components of robot and agent technology, combined with learning theories and methodologies, can develop powerful learning partnerships. Exploring ways to leverage the affordances of technology as peers and learning tools can provide teachers with useful information to identify features and conditions for learning. This in turn can help design activities using pedagogical robots/agents to assist collaboration with and between students.

Robots Pedagogical agents Adaptive support Tutoring systems

Collaborative Learning Analytics
Alyssa Friend Wise, Simon Knight, Simon Buckingham Shum
Pages 425-443

The use of data from computer-based learning environments has been a long-standing feature of CSCL. Learning Analytics (LA) can enrich this established work in CSCL. This chapter outlines synergies and tensions between the two fields. Drawing on examples, we discuss established work to use learning analytics as a research tool (analytics of collaborative learning—ACL). Beyond this potential though, we discuss the use of analytics as a mediational tool in CSCL—collaborative learning analytics (CLA). This shift raises important challenges regarding the role of the computer—and analytics—in supporting and developing human agency and learning. LA offers a new tool for CSCL research. CSCL offers important contemporary perspectives on learning for a knowledge society, and as such is an important site of action for LA research that both builds our understanding of collaborative learning and supports that learning.

Learning analytics Educational data mining Collaboration Collaborative learning Computer-supported collaborative learning Intelligent support for groups Adaptive support for groups

Tools and Resources for Setting Up Collaborative Spaces
Carolyn Rosé, Yannis Dimitriadis
Pages 445-460

Great strides have been made in the field of CSCL toward fostering diversity at all levels including theory, methods, and technologies. This chapter provides a reflection on the field from the standpoint of the endeavor to provide tools to expedite the work we do as learning scientists. It points to some notable existing resources while also exploring the reasons why the development of high-profile, wide distribution tools to support the work has not been a priority for the community. It then provides a vision for future work that respects these reasons but points to ways community resources might be better served with greater care and attention allocated to this vision.

Tools Technology CSCL platforms


Front Matter
Pages 461-461

Case Studies in Theory and Practice
Timothy Koschmann, Baruch B. Schwarz
Pages 463-478


What sets CSCL research apart is a principled commitment to learning in settings of collaboration. This commitment necessitates developing a foundational understanding of how participants build meaning together in practical situations. Case studies are a traditional means of investigating such matters. Researchers must be cognizant, however, of the assumptions underlying their approach. Historically, case studies have been undertaken within multiple disciplines and from a variety of theoretical perspectives. We provide here a set of examples in CSCL research. Questions that arise include: What is being construed as a “case?” How was it selected? What forms of contrast are built into the analysis and to what end? What is the role of time and sequence within the analysis? Does the study seek to alter the social phenomenon under investigation or merely document it faithfully? As case studies become a more prominent feature of CSCL research, we need to develop a keener appreciation of these issues.

Ethnography Cultural-Historical Activity Theory (CHAT) Critical Theory Dialogic Theory Actor-Network Theory (ANT) Ethnomethodology Conversation Analysis (CA)

Design-Based Research Methods in CSCL: Calibrating our Epistemologies and Ontologies
Yael Kali, Christopher Hoadley
Pages 479-496

Design-based research (DBR) methods are an important cornerstone in the methodological repertoire of the learning sciences, and they play a particularly important role in CSCL research and development. In this chapter, we first lay out some basic definitions of what DBR is and is not, and discuss some history of how this concept came to be part of the CSCL research landscape. We then attempt to describe the state-of-the-art by unpacking the contributions of DBR to both epistemology and ontology of CSCL. We describe a tension between two modes of inquiry—scientific and design—which we view as inherent to DBR, and explain why this has provoked ongoing critique of DBR as a methodology, and debates regarding the type of knowledge DBR should produce. Finally, we present a renewed approach for conducting a more methodologically coherent DBR, which calibrates between these two modes of inquiry in CSCL research.

Design-based research (DBR) CSCL epistemology CSCL ontology Methodological alignment Design researchers’ transformative learning (DRTL)

Experimental and Quasi-Experimental Research in CSCL
Jeroen Janssen, Ingo Kollar
Pages 497-515

(Quasi-)experimental designs play an important role in CSCL research. By actively manipulating one or several independent variables while keeping other influencing factors constant and through the use of randomization, they allow to determine the causal effects of such independent variables on one or more dependent variable(s) that may be of interest to CSCL researchers. So far, (quasi-)experimental CSCL studies have mainly looked at the effects of certain tools and scaffolds on the occurrence of hoped-for learning process and outcomes variables. While earlier CSCL research mainly ignored the interdependence of data from learners who learned in the same group, more recent research uses more advanced statistical methods to analyze the effects of different CSCL settings on learning processes and outcomes (such as multilevel modeling). Because of the replication crisis in psychology, preregistration and the open science movement are becoming increasingly important also for CSCL research that uses (quasi-)experimental designs.

Computers-supported collaborative learning Experimental research Quasi-experimental research Reproducibility Open science

Development of Scalable Assessment for Collaborative Problem-Solving
Yigal Rosen, Kristin Stoeffler, Vanessa Simmering, Jiangang Hao, Alina von Davier
Pages 517-532

While the field of computer-supported collaborative learning (CSCL) is focused primarily on the development of computational artifacts and social interaction, the key research advances in collaborative problem-solving (CPS) domain are associated with competency model development and assessment at scale. Numerous research reports indicate that CPS competency is increasingly important in today’s complex interconnected world, therefore, of increasing interest in teaching and assessing with students. However, learning and assessment design, data analytics, and reporting on CPS competency, specifically in CSCL setting encapsulates multiple opportunities and challenges. This chapter introduces a spectrum of approaches for CPS competency development and scalable assessment to advance the theory and practice of measuring CPS with the focus on recent work at the nonprofits ACT and Educational Testing Service.

Collaboration assessment Problem-solving assessment Collaborative problem solving Conversational agent Collaborative learning

Statistical and Stochastic Analysis of Sequence Data
Ming Ming Chiu, Peter Reimann
Pages 533-550

Two common CSCL questions regarding analyses of temporal data, such as event sequences, are: (i) What variables are related to event attributes? and (ii) what is the process (or what are the processes) that generated the events? The first question is best answered with statistical methods, the second with stochastic or deterministic process modeling methods. This chapter provides an overview of statistical and stochastic methods of direct relevance to CSCL research. Many of the statistical analyses are integrated into statistical discourse analysis. From the stochastic modeling repertoire, the basic hidden Markov model as well as recent extensions is introduced, ending with dynamic Bayesian models as the current best integration. Looking into the near future, we identify opportunities for a closer alignment of qualitative with quantitative methods for temporal analysis, afforded by developments such as automization of quantitative methods and advances in computational modeling.

Statistical discourse analysis Time analysis Stochastic models Process mining

Artifact Analysis
Stefan Trausan-Matu, James D. Slotta
Pages 551-567

Artifacts are constructed as a result of human activity. They are the tools for further activities and the basis for communication and collaboration. Within any given learning context, artifacts may be produced and can serve as a basis for assessment as well as resources for subsequent activity, being semiotic mediators. Learning scientists analyze artifacts as a method of evaluating their own interventions and to informing their understanding of learning processes. This chapter provides a short review of relevant theoretical perspectives and prior research and describes different forms of language and text artifact analysis that are presently applied within the learning sciences. These include dialog analysis; conversation analysis; content analysis of verbal, textual, and other forms of data; social network analysis; and polyphonic analysis. Applications to the analysis of online discussions and classroom discourse are discussed, as well as future directions for research.

Artifacts Computer-supported collaborative learning Analysis of conversations Natural language processing Social network analysis Polyphonic model

Finding Meaning in Log-File Data
Jun Oshima, H. Ulrich Hoppe
Pages 569-584

This chapter will start with a characterization of log-file data and related examples and then elaborate on ensuing levels of processing, interpretation/meaning-making, and finally support for decision-making and action (“actionable insights”). According to the characteristic of log files as sequences of action descriptions, we will set our focus on what has been called “Action Analysis” as compared to “Discourse Analysis.” Following up on the characterization of input data, we will review computational techniques that support the analysis of log files. Techniques of interest include process-oriented approaches (such as process mining, sequence analysis, or sequential pattern mining) as well as approaches based on social network analysis (SNA). Such techniques will be further discussed regarding their contribution to data interpretation and meaning-making. Finally, the future direction of log-file analysis is discussed considering the development of new technologies to analyze spoken conversation and nonverbal behaviors as part of action–log data.

Log-file data Action analysis Discourse analysis Process-oriented approach Social network analysis

Quantitative Approaches to Language in CSCL
Marcela Borge, Carolyn Rosé
Pages 585-604

In this chapter, we provide a survey of language quantification practices in CSCL. We begin by defining quantification of language and providing an overview of the different purposes it serves. We situate language quantification within the spectrum of more to less quantitative research designs to help the reader understand that both quantitative and qualitative researchers can quantify language. We then provide a review of articles published in IJCSCL from 2006 to 2018 to provide the reader with an understanding of who is quantifying language, in what contexts, how they are quantifying it, and for what purpose. The articles were sorted by theoretical stance to show how theoretical leanings influence (1) how researchers perceive language as a tool for learning and (2) how they quantify language. Finally, we discuss the future of language quantification in CSCL, including grand challenges we face, emerging practices, and new directions.

Quantification of language Computer-supported collaborative learning Quantitative methods Learning theories Theoretical frameworks Methodological frameworks Survey

Qualitative Approaches to Language in CSCL
Suraj Uttamchandani, Jessica Nina Lester
Pages 605-623

In this chapter, we discuss qualitative approaches to the study of language and discourse and their potential relevance for CSCL researchers. We begin by overviewing these approaches generally. Next, we discuss how language-based methodologies have historically been used in CSCL. We contextualize two of the more common methodological approaches in the field: conversation analysis and interaction analysis. Next, we discuss two methodological approaches to discourse analysis that have not yet seen wide use in CSCL but that we argue are of relevance to the field: critical discourse analysis and discursive psychology. For each approach, we briefly outline its history, analytic process, and quality markers and provide an illustrative example. We conclude by discussing the challenges and possibilities for using qualitative approaches to language in CSCL research.

Computer-mediated communication Interaction analysis Conversation analysis Critical discourse analysis Discursive psychology

Gesture and Gaze: Multimodal Data in Dyadic Interactions
Bertrand Schneider, Marcelo Worsley, Roberto Martinez-Maldonado
Pages 625-641

With the advent of new and affordable sensing technologies, CSCL researchers are able to automatically capture collaborative interactions with unprecedented levels of accuracy. This development opens new opportunities and challenges for the field. In this chapter, we describe empirical studies and theoretical frameworks that leverage multimodal sensors to study dyadic interactions. More specifically, we focus on gaze and gesture sensing and how these measures can be associated with constructs such as learning, interaction, and collaboration strategies in colocated settings. We briefly describe the history of the development of multimodal analytics methodologies in CSCL, the state of the art of this area of research, and how data fusion and human-centered techniques are most needed to give meaning to multimodal data when studying collaborative learning groups. We conclude by discussing the future of these developments and their implications for CSCL researchers.

Multimodal sensing Learning analytics Eye-tracking Motion sensing Colocated collaborative learning Computational models

Video Data Collection and Video Analyses in CSCL Research
Carmen Zahn, Alessia Ruf, Ricki Goldman
Pages 643-660

The purpose of this chapter is to examine significant advances in the collection and analysis of video data in computer-supported collaborative learning (CSCL) research. We demonstrate how video-based studies create robust and dynamic research processes. The chapter starts with an overview of how video analysis developed within CSCL by way of its pioneering roots. Linked throughout the chapter are the theoretical, methodological, and technological advances that keep advancing CSCL research. Specific empirical and experimental research examples will illustrate current and future advances in data collection, transformation, coding, and analysis. Research benefits and challenges that include the current state of understanding from observations of single, multiple, or 360° camera recordings will also be featured. In addition, eye-tracking and virtual reality environments for collecting and analyzing video data are discussed as they become new foci for future CSCL research.

Video data Video analysis Learning research Group research Psychological methods

Back Matter
Pages 661-680

4 thoughts on “Notes on Computer Supported Collaborative Learning Handbook 2021

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