The Effect of Communication Styles on Computer-Supported Collaborative Learning

The Effect of Communication Styles on Computer-Supported Collaborative Learning

Hichang Cho (National University of Singapore, Singapore) and Geri Gay (Cornell University, USA)
DOI: 10.4018/978-1-60566-392-0.ch017
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This chapter investigates the relationships between communication styles, social networks, and learning in a Computer-Supported Collaborative Learning (CSCL) community. Using Social Network Analysis (SNA) and longitudinal survey data, the authors analyzed how 31 distributed learners developed collaborative learning social networks, when they had work together on the design of aerospace systems using online collaboration tools. The results showed that both learner’s personality characteristics (communication styles) and structural factors (a pre-existing friendship network) significantly affected the way the learners developed collaborative learning social networks. More specifically, learners who possessed high Willingness to Communicate (WTC) or occupied initially peripheral network positions were more likely to explore new network linkages in a distributed learning environment. The authors propose that the addition of personality theory (operationalized here as communication styles) to structural analysis (SNA) contributes to an enhanced picture of how distributed learners build their social and intellectual capital in the context of CSCL.
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A growing body of research has demonstrated that communication and conversation are central elements in collaborative learning environments (Harasim, Hiltz, Teles, & Turoff, 1995; Haythorthwaite, 2002). From the social network perspective, learning is a social and collective outcome achieved through seamless conversations, shared practices, and networks of social connections (Brown & Duguid, 1991). Knowledge, in this sense, is not a static object acquired by an atomic individual but is actively co-constructed through ongoing social exchanges and collaborations among multiple learners embedded in social networks (Cohen & Prusak, 1998; Lave & Wenger, 1991; Nonaka & Konno, 1998). Social networks also play instrumental roles in learning environments as a major conduit of resource and knowledge exchanges (Cho, Stefanone, & Gay, 2002) and as a source of social support and socialization for distributed learners (Haythorthwaite, 2002). Hence, the way individuals create social capital—or the way they are situated in social networks from the structuralist point of view—should significantly influence the acquisition, construction, and exchange of knowledge.

Theoretically, there are abundant discussions emphasizing the value and the impact of social networks in the studies of organizational learning (Nahaphiet & Goshal, 1998), knowledge management (Cohen & Prusak, 1998), and distance learning (Haythorthwaite, 2002). Empirically, however, very few studies have actually examined the “origins” of social networks in actual Computer-Supported Collaborative Learning (CSCL) or Cooperative Work (CSCW) settings (Millen, Fontaine, & Muller, 2002; Woodruff, 2002). In other words, relatively little research has been conducted to explicitly examine what factors influence the creation of different social networks in the context of CSCL, or why some learners occupy structurally advantageous positions than others. This is surprising, as “individual differences” have long been a central variable in educational research (Ellis, 2003; Scalia & Asckmary, 1996; Webb & Palincsar, 1996).

The aim of this study is to identify individual and structural factors that influence the way people develop emergent collaborative social and collaborative structures. In particular, we focus on how learners’ personality characteristics (operationalized here as communication styles) interact with social-structural elements of collaborative learning (a pre-existing social network) to influence learning activities in a distributed learning environment. By adding personality theory to structural analysis (SNA), we attempt to contribute to an enhanced picture of how distributed learners build their social and intellectual capital in the context of CSCL.

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Editorial Advisory Board
Table of Contents
Constantinos Mourlas, Nikos Tsianos, Panagiotis Germanakos
Chapter 1
Michael Grimley, Richard Riding
This chapter considers a range of individual difference variables that have potential relevance to specifically designed Web-based learning... Sample PDF
Individual Differences and Web-Based Learning
Chapter 2
Steve Rayner
The need to personalize Web-based learning environments on individuals is the main argument of this chapter. The activation of a “differential... Sample PDF
Personalizing Style in Learning: Activating a Differential Pedagogy
Chapter 3
Martin Graff
This chapter considers the question of whether Web-based learning environments can be employed to effectively facilitative learning. Several... Sample PDF
Can Cognitive Style Predict How Individuals Use Web-Based Learning Environments?
Chapter 4
Michael Workman
Online education has experienced phenomenal growth, but some researchers and practitioners, as well as some students, have raised serious questions... Sample PDF
Cognitive Styles and Design Interactions in Web-Based Education
Chapter 5
George Spanoudis, Eleni A. Kyza
This chapter outlines key findings of cognitive and developmental psychology which could be used as a theoretical framework to guide the design and... Sample PDF
Integrating Knowledge of Cognitive System and E-Learning Applications
Chapter 6
Zoe Bablekou
The path to the study of cognition has to take into account working memory, as it is a key process of thinking operations in the human cognitive... Sample PDF
Nous: Cognitive Models of Working Memory
Chapter 7
Makis Leontidis, Constantin Halatsis
Research in computer science recently began to take emotions into account because their influence in perception, reasoning, decision-making and... Sample PDF
Affective Issues in Adaptive Educational Environments
Chapter 8
Elena C. Papanastasiou, Aimilia Tzanavari, Patricia Lowe
Testing is an integral part of the learning process that aims to estimate the learner’s abilities as accurately and efficiently as possible. This... Sample PDF
Understanding Learner Trait, Test and Computer Anxiety in the Context of Computer-Based Testing
Chapter 9
Nikos Tsianos, Panagiotis Germanakos, Zacharias Lekkas, Costas Mourlas
The purpose of this chapter is to experimentally explore the effect of individual differences in an adaptive educational hypermedia application. To... Sample PDF
Individual Differences in Adaptive Educational Hypermedia: The Effect of Cognitive Style and Visual Working Memory
Chapter 10
Christian Gütl, Victor Manuel García-Barrios
Due to the wide diversity of learning styles and learner characteristics, delivering learning material from modern ICT-based learning must also be... Sample PDF
The DEKOR System: Personalization of Guided Access to Open Repositories
Chapter 11
Elvira Popescu
Individualizing the learning experience for each student is an important goal for educational systems and accurately modeling the learner is the... Sample PDF
Diagnosing Students' Learning Style in an Educational Hypermedia System
Chapter 12
Ray Adams, Andrina Granic
The creation of exciting, new, powerful and accessible e-learning systems depends upon innovations in cognitive science, human learning, e-learning... Sample PDF
Cognitive Learning Approaches to the Design of Accessible E-Learning Systems
Chapter 13
William Billingsley, Peter Robinson
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Intelligent Books: Combining Reactive Learning Exercises with Extensible and Adaptive Content in an Open-Access Web Application
Chapter 14
Jorge Ferreira Franco, Irene Karaguilla Ficheman, Marcelo Knörich Zuffo, Valkiria Venâncio
This chapter addresses an ongoing work strategy for developing and sharing knowledge related to digital/ Web-based technology and multimedia tools... Sample PDF
Enhancing Individuals' Cognition, Intelligence and Sharing Digital/Web-Based Knowledge Using Virtual Reality and Information Visualization Techniques and Tools within K-12 Education and its Impact on
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Robert Z. Zheng, Jill A. Flygare, Laura B. Dahl, Richard R. Hoffman
This chapter describes the college students’ online social communication patterns and behavior with a focus on the impact of individual differences... Sample PDF
The Impact of Individual Differences on Social Communication Pattern in Online Learning
Chapter 16
Yin Zhang
Collaborative learning has long been proven to be an effective approach in the traditional classroom setting. Despite the discussion of the benefits... Sample PDF
Collaborative Learning in a Web-Based Environment: A Comparison Study
Chapter 17
Hichang Cho, Geri Gay
This chapter investigates the relationships between communication styles, social networks, and learning in a Computer-Supported Collaborative... Sample PDF
The Effect of Communication Styles on Computer-Supported Collaborative Learning
Chapter 18
Jan-Willem Strijbos, Theresa A. Ochoa, Dominique M.A. Sluijsmans, Mien S.R. Segers, Harm H. Tillema
Extant literature on collaborative learning shows that this instructional approach is widely used. In this chapter, the authors discuss the lack of... Sample PDF
Fostering Interactivity through Formative Peer Assessment in (Web-Based) Collaborative Learning Environments
Chapter 19
F. Pozzi
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Supporting Group and Individual Processes in Web-Based Collaborative Learning Environments
Chapter 20
Andrina Granic, Maja Cukušic, Aimilia Tzanavari, George A. Papadopoulos
Web-based learning environments have become an integral part of learning. The way that they are employed in the learning process, or in other words... Sample PDF
Employing Innovative Learning Strategies Using an E-Learning Platform
Chapter 21
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