Technology in Support of Collaborative Learning

Abstract

This paper reviews the research conducted in the last 20years on the application of technology in support of collaborative
learning in higher education. The review focuses primarily on studies that use Internet-based technologies and social interaction
analysis. The review provides six sets of observations/recommendations regarding methodology, empirical evidence, and research
gaps and issues that may help focus future research in this emerging field of study.

Full text

ORIGINAL ARTICLE
Technology in Support of Collaborative Learning
Paul Resta & Thérèse Laferrière
Published online: 31 January 2007
#
Springer Science + Business Media, LLC 2007
Abstract This paper reviews the research conducted in the last 20 years on the application
of technology in support of collaborative learning in higher education. The review focuses
primarily on studies that use Internet-based technologies and social interaction analysis.
The review provides six sets of observations/recommendations regarding methodology,
empirical evidence, and research gaps and issues that may help focus future research in this
emerging field of study.
Keywords Collaborative learning
.
Technology for collaborative learning
The recent interest in technology-supported collaborative learning in higher education
represents a confluence of trends: the development of new tools to support collaboration
(Johnson & Johnson, 1996), the emergence of constructivist-based approaches to teaching
and learning (Kirschner, Martens, & Strijbos, 2004), and the need to create more powerful
and engaging learning environments (Oblinger & Oblinger, 2005). Chickering’s seven
principles for good practice in undergraduate education (Chickering & Ehrmann, 1996)have
been widely adopted, and technology has often been used in their implementation. Two of
Chickering’s principles relate directly to cooperative/collaborative learning: “Good practice
develops reciprocity and cooperation among students,” and “Good practice uses active
learning techniques.”
Although a variety of technologies may be used to support cooperative/collaborative
learning, this paper focuses on the ways computer-mediated networks support social
interaction, cooperation, and collaboration, for learning and knowledge building. Current
Educ Psychol Rev (2007) 19:65–83
DOI 10.1007/s10648-007-9042-7
P. Resta (*)
Department of Curriculum and Instruction, Learning Technology Center,
The University of Texas at Austin, 1 University Station D5900, Austin, TX 78712, USA
e-mail: resta@mail.utexas.edu
T. Laferrière
Laval University, Quebec City, QC, Canada

practices include technology-rich learning environm ents, network-enhanced l earning
environments, blended/hybrid learning environments (combining face-to-face and online
interaction), and virtual learning environments.
1
Harasim, Hiltz, Teles, and Turoff (1995)
defined online collaborative learning as “a learning process where two or more people work
together to create meaning, explore a topic, or improve skills.” At the outset of this review,
it must be acknowledged that collaborative learning is a complex concept and not a clearly
defined one. There is no univers ally adopted meaning of the term s ‘ collaborative’ and
‘cooperative’ learning or agreement on precisely what their differences or common-
alities are. This may result from the fact that educational researchers often have
different purposes, goals, and perspectives, w hich prohibit a clear distinction between
these two approaches. Panitz (1996) views collaboration as a philosophy of interaction
and personal lifestyle, while cooperation is viewed as a structure of interaction designed
to facilitate accomplishment of an end product or goal through people working together in
groups. Slavin (1997) associates cooperative learnin g with well-structured knowledge
domains, and collaborative learning with ill-structured knowledge domains. Roschelle
and Teasley (199 5) state that: “Cooperation is accomplished by the division of labor
among participants, as an activity where each person is responsible for a portion of the
problem solving...” while collaborative learning involves the “... mutual engagement of
participants in a coordinated effort to solve the problem together” (p. 70). Dillenbourg
(1999)agreeswhenhestressedthat“cooperation refers to a more fixed division of labor ”
(p. 22). Despite the differences drawn between these two constructs, Kirschner (2001)
indicates that both share a number of common elements including:
& Learning is active
& The teacher is usually more a facilitator than a “ sage on the stage”
& Teaching and learning are shared experiences
& Students participate in small-group activities
& Students take responsibility for learning
& Students reflect on their own assumptions and thought processes
& Social and team skills are developed through the give-and-take of consensus- building
These similarities closely align with the view of Johnson and Johnson (1996) that
collaborative and cooperative learning both involve the instructional use of small groups in
which students work together to maximize their own and each other’s learning.
Globally, the growth in the use of technology to support collaborative learning in
higher education has attracted a rapidly growing number of research studies focused on
some aspect of technology-supported collaborative learning examined from different
theoretical perspectives. As the boundaries of the research expand, the confluence of the
trends suggest a movement towards the understanding of Technology in Support of
Collaborative Learning as an emerging field of study. The intention of this paper is to
provide the reader with a sampling of the important research issues, challenges, and
directions in the emerging area of research. A set of observations/recommendations that
take into account methodological issues and empirical evidence, as well as research
gaps, is presented.
1
Computer-supported collaborative le arning is encoura ged in campus-based classrooms or distance
education. In the latter case see (IHEP, 2001; WASC, 2000).
66 Educ Psychol Rev (2007) 19:65–83

The Emerging Paradigm of Computer-Supported Collaborative Learning (CSCL)
The term computer-supported collaborative learning was used as early as 1989 by O’Malley
and Scanlon and was recognized by Koschmann as an important area of research focus in
1996 (Lipponen, Hakkarainen, & Paavola, 2004). CSCL is emerging as a dynamic,
interdisciplinary, and international field of research focused on how technology can
facilitate the sharing and creation of knowledge and expertise through peer interaction and
group learning processes. The CSCL field of inquiry includes a range of situations in which
interactions take place among students using computer networks to enhance the learning
environment. It includes the use of technology to support asynchronous and synchronous
communication between students on-campus as well as students who are geographically
distributed. The primary aim of CSCL is to provide an environment that supports
collaboration between students to enhance their learning processes (Kreijns, Kirschner, &
Jochems, 2003), facilitate collective learning (Pea, 1994), or group cognition (Stahl, 2006).
Researchers typically draw upon theoretical frameworks and constructs derived from
constructivist epistemology (Piaget) and cognitive science’s theoretical perspectives
emphasizing that cognition is a social rather than a fixed entity (e.g., Levine, Resnick,
&Higgins1993; anchored instruction, Bransford, Sherwood, Hasselbring, Kinzer, &
Williams, 1990; cognitive apprenticeship, Brown, Collins, & Duguid, 1989; cognitive
flexibility theory, Spiro, Coulson, Feltovich, & Anderson, 1988; collaborative visualization,
Pea, 1994; distributed cognition, Hutchins, 1995; Salomon, 1993; Salomon & Perkins, 1998;
distributed constructionism, Kafai & Resnick, 1996;groupcognition,Stahl,2006; knowledge
building, Bereiter & Scardamalia, 1993; learning communities, Brown, 1997; situated
cognition, Lave & Wenger, 1991). Research in CSCL is also increasingly becoming a trans-
disciplinary field of inquiry including cognitive science, learning sciences (psychology,
computer science, education), educational psychology, educational technology, communi-
cation, epistemology, social psychology (small group research), artificial intelligence, and
informatics (group support systems).
A review of the CSCL research literature demonstrates a diversity of approaches and
methodol ogies used, rangin g from experimental to ethnography, action, and design
research. The methods vary by the type of theoretical framework employed by the
researcher, but often share a common focus on the interaction, discourse, and the
participation processes emerging among community members in particular social and
physical contexts (Lipponen et al., 2004). Studies also vary in their level of analysis.
Research may focus on group and classroom/community processes adopting analytical
frameworks such as activity or systems theory (Gifford & Enyedy, 1999; Jones, Dirckinck-
Holmfeld, & Lindström, 2006) or may focus on peer-to-peer interaction adopting analytical
frameworks such as interaction analysis and small group processes. Methods of analysis
include graphing note distributions and coding the semantics of the notes through the use of
systematic and emergent observation categories, as well as coding schemes of a generic or
specific nature, e.g., Interaction Process Analysis, (Bales, 1950); Gunawardena, Lowe, and
Anderson (1997); social network analysis (Wasserman & Faust, 1994) and latent semantic
analysis (Landauer, Foltz, & Laham, 1998). Methods to analyze participation patterns have
also been developed (Howell-Richardson & Mellar, 1996).
The review included theoretical research, peer-reviewed case studies, as well as design
research and experiments. It is increasingly clear that researchers are faced with a difficult
challenge to take into account the great diversity of research results in their research
perspective, so that their contributions build on, and go beyond, what is known. As an
object of design or inquiry, the technology itself provides focus. For instance, research on
Educ Psychol Rev (2007) 19:65–83 67

CSCL environments, although emerging from differing theoretical approaches, evolves
around specific computer applications (i.e., platforms, forums, videoconferencing systems).
The designed environment may be totally online or hybrid (including both online and
classroom interaction) but both have a technical and a social dimension. There is a substantial
body of knowledge on collaborative learning in face-to-face settings, but less is known about
CSCL. Research activity examining technology use in support of collaborative learning
occurs in many different contexts because of the widescale infusion of electronic resources
into learning environments, including ones that support asynchronous and synchronous
communication between geographically distributed individuals. Because of the growing use
of CSCL, questions are asked about the effectiveness of this approach to teaching and
learning.
To assess the potential of a particular technology for supporting collaborative learning,
researchers usually rely on specific analytical models. It is evident that multiple factors are at
play in face-to-face and online learning environments including pedagogical strategies,
context, interaction with peers and instructor, and assessment (Laurillard, 2001). Systemic
models are useful for situating technology use within a broader context. For instance,
Engeström’s(1987) activity theory framework allows researchers to assess activity change
within a technology-supported learning community by analyzing role shifts, emerging rules
and routines, and new learning and knowledge-building artifacts. There are also
frameworks that may be useful in assessing the added value of technology support for
collaborative learning in higher education. One such framework is Biggs’ (1989) 3Ps
generic model that includes:
& Presage variables provide the context in which a learning experience is conducted and
includes the instructor, curriculum, learning environment, and learner characteristics.
& Process variables include the interventions, interactions, pedagogical approaches,
duration of educational experiences, type of student participation, assessment, use of
distance learning, etc.
& Product variables include the quality of learning outcomes.
This framework was used in reviewing the growing body of research in technology-
supported collaborative learning and in offering recommendations that may help address the
challenges in developing coherence from the diversity of theoretical approaches, methodol-
ogies, contexts, subject matter domains, and learners in this emerging field of study.
Recommendation 1. To afford greater potential for replicability, researchers are
encouraged to conduct evidence-based research providing thick descriptions of the
participants, contextual elements, and analysis methods.
It is challenging to compare and analyze CSCL studies because of the divergent views
of what should be studied and how it should be studied. Contributing to the complexity
in developing a coherent view are differences in the ways CSCL is defined and whether
one should primarily study the effects of or effects with CSCL (Lipponen, 2002). CSCL
studies also vary in the learning context and knowledge domain (e.g., undergraduate science,
second-language education, graduate engineering course), the complexity and duration of
learning tasks, the type and size of the groups, and the number of participants, to mention a
few.
There are also differences in methodological approaches, ranging from descriptive studies
using ethnography, discourse analysis, interaction analysis, and qualitative research
procedures, to established and widely accepted educational experimental research paradigms.
68 Educ Psychol Rev (2007) 19:65–83

The differences in methodological approaches are reflected in views such as those of
Valcke and Martens (2006), who underscore the need for the design and development of
more valid and reliable instruments and methodologies in CSCL studies. Strijbos, Martens,
Prins, and Jochems (2006) add that only a few studies included in proceedings of CSCL
conferences provide psychometric data or information about the reliability and/or validity
of the measures employed in the studies. The lack of this information limits the ability to
replicate or build upon the research findings. Similarly, De Wever, Schellens, Valcke,
and Van Keer (2006), in reviewing CSCL research, indicate that a large number of
instruments used for content analysis in CSCL studies have a weak theoretical and
empirical basis. They also note the lack of replication studies that may strengthen the
quality of existing content analysis instruments. Other researchers point out that
collaborative knowledge building is a complex and subtle process that is not easily studied
using traditional experimental approaches. They put the emphasis on process accounts
(Holliman & Scanlon, 2006; Roschelle & Teasley, 1995) to yield insights on the
relationships between the nature of social and cognitive processes and successful learning
and knowledge building.
All of these issues are reflective of a new and emerging field of research that continues
to grow, both in the quantity and focus of studies. There is also a realization of the need to
further develop the theoretical-empirical bases of CSCL research and new journals such as
the International Journal for Computer Supported Collaborative Learning (IJCSCL) are
being created to support these efforts.
Recommendation 2. Future CSCL studies should focus less attention on the question
of whether computer-supported collaborative learning is better than face-to-face
collaborative learning, but rather focus on what is uniquely feasible with new
technology (group cognition, collaborative knowledge building) and the different
ecologies and affordances of CSCL environments and tools that are diverging further
and further from face-to-face learning environments.
The review identified four instructional motives for the use of technology in support of
collaborative learning:
To prepare students for the knowledge society (collaboration skills and knowledge creation)
Teachers respon d to the social de mands of a highly diverse, interdependent, and
technologically rich workplace that has undergone an explosive development of knowledge
in many fields that calls for teamwork (UNESCO, 2005). Schrage (1990) conceptualized
collaboration as “the process of shared creation.” Referring to Andriessen, Baker, and Suthers
(2003) and Bereiter (2002), Wegerif (2006) emphasized the historical shift in work and life
practices. He posited an argument for broadening and deepening the way students engage
in online dialogue when thinking skills are the pedagogical intent: “constructing
representations with cognitive tools needs to be balanced and augmented by the
metaphorical image of stepping back from identity commitments in order to actively
listen to others and thereby to deepen and expand creative dialogic spaces of reflection.”
(p. 156).
To enhance student cognitive performance or foster deep unders tanding Rationales
underlying the use of CSCL for enhancing cognitive performance (e.g., Rimmershaw,
1999) or fostering deep understanding (Stone-Wiske, 2002) are similar to those fostering
cooperative/collaboration learning opportunities without the use of networked computers
Educ Psychol Rev (2007) 19:65–83 69

(Guimond, 2001; Johnson, Johnson, & Smith, 1998b; Monteil & Huguet, 1999; Slavin,
1996). On-site and online social interaction is considered a source of cognitive
advancement, and may play an important role in academic achievement.
To add flexibility of time and space for cooperative/collaborative learning The new
workspace is increasingly a virtual one in which work is done by individuals who are
distributed in place and time. Based on this trend, instructors want to create opportunities
for their students to learn to work independently of place and time (e.g., Collis & Moonen,
2001; Palmieri, 1997).
To foster student engagement and keep track of student cooperative/collaborative work
(online written discourse) Research has linked collaborative tasks to student engagement
in knowledge construction (Brett, 2004; Stahl, 2004). Moreover, instructors who use CSCL
can monitor student understanding and achievement in collaborative learning activities
(Holliman & Scanlon, 2006). In addition, students can review what they wrote or what
their peers wrote, and instructors can analyze the discourse of team members using semi-
automatic data analysis procedures for facilitation, moderation, or grading purposes.
The use of CSCL also needs to be justified in terms of the benefits to students (product
variables). The benefits of cooperative learning in face-to-face settings are well established
(Johnson, Johnson, & Smith, 1998a). There is emerging evidence of the learning benefits of
CSCL. These include development of higher order thinking skills, student satisfaction with
the learning experience, and improved productivity. Currently, however, research is still
shallow regarding product variables:
& Academic Achievement The results of studies examining cooperative, competitive, and
individualistic learning using computers (Johnson & Johnson, 1989; Johnson et al.,
1998b; Johnson, Johnson, Stanne, & Garibalde, 1990; Johnson, Johnson, Stanne,
Smizak, & Avon, 1987) found that computer-assisted cooperative learning yields higher
quantity and quality of daily achievement, greater mastery of factual information, and
greater success in problem solving than computer-supported individualistic learning. In
higher education, similar results were found: MBA students who engaged in
collaborative learning using a group decision support system obtained test grades
significantly higher than those of the other group of students who participated in
the experiment (Alavi, 1994 ); students in industrial technology achieved better
critical thinking through collaborative learning (Gokhale, 1995).
& Development of Higher order Thinking Skills. There is research supporting the idea
that online environments are as powerful as, or more so, than campus-based classes
(Lockyer, Patterson, & Harper, 2001; Mason & Romiszowski, 1996). Students report
higher levels of learning in online compared to face-to-face groups. Researchers found
that online groups, compared to face-to-face groups, engaged in broader, more complex,
and more cognitively challenging discussions (Benbunan-Fich, Hiltz, & Turoff, 2003).
Thinking skills have been emphasized as a focus of online discourse by a number of
researchers (Wegerif, 2006).
& Student Satisfaction. The evidence is strong and consistent across a broad array
of educational research studies that students engaged in peer interaction, whether face-
to-face, online or both, have more positive attitudes toward subject matter, increased
motivation to learn more about the subject, and are better satisfied with the experience
than students who have few opportunities to interact with their peers and the instructor
70 Educ Psychol Rev (2007) 19:65–83

(Johnson et al., 1998a; Springer, Stanne, & Donovan, 1998). Students preferred to
collaborate in the traditional face-to-face manner but, when working online, they were
just as satisfied with the end product as when working onsite (Ocker & Yaverbaum,
2001).
& Individual and Group Products. When compared to face-to-face groups, online groups
deliver more complete reports, make decisions of higher quality, and perform better on
tasks that require groups to generate ideas (Benbunan-Fich et al., 2003; Fjermestad,
2004).
& Group Cognition. Stahl’s proposal (2006) to focus CSCL research on group cognition
expands and limits the domain and clarifies the specificity of CSCL research. Group
cognition, argues Stahl, is visible online because participants’ building of knowledge
can be observed through individual contributions and their linkages. We suggest that
CSCL basic elements could be uncovered, as did Johnson and Johnson (1989) for
cooperative learning when t hey suggested positive interdependence, individual
accountability, promotive interaction, appropriate use of social skills, and group
processing as core elements of effective cooperative learning.
Recommendation 3. Researchers are encouraged to apply what they know about face-
to-face collaborativ e learning in their analysis of online interaction in CSCL
environments.
An extensive base of knowledge has been developed related to cooperative learning
without online technical support. Although technology affords new tools and environments
to support collaborative learning, many of the goals, pedagogical strategies, and interactions
are similar. Productive lines of research could be based on what is common to both
environments, as well as what is unique to technology-supported collaboration. Critical
elements for effective CSCL include the development of instructional goals that target
higher-order thinking skills and complex problem solving (Dirckinck-Holmfeld, 2002;
Hmelo-Silver, 2004). Successful collaboration requires the careful design of the learning
environment for group interaction and the provision of scaffolding, leadership, and support
by the instructor (Pea, 2004; Strijbos, Kirschner, & Martens, 2004) to facilitate meaning-
making by the students. Our review highlights the fact that basic factors continue to
influence pe er-to-peer interaction when technology is us ed to support collaborative
learning. Technology affordances, however, facilitate the teacher’s task, as the following
sections suggest.
Group Composition
There is limited research in CSCL on effects of the size of the group. But there is
recognition that group size depends on the scope, duration, and complexity of the task. The
learning group, however, needs to be small enough to enable students to participate fully
and to build group cohesion (Barkley, Cross, & Major, 2005; Schellens & Valcke, 2006).
Bean (1996) asserts a group size of five may be optimal for many learning situations
because larger groups may dilute the experience for the learner. In formal learning tasks,
groups of four tend to break into pairs, and groups of three split into a pair and an outsider.
Groups of three, however, work effectively for base groups (Smith, 1996).
Educ Psychol Rev (2007) 19:65–83 71

There are a variety of ways in which groups can be constituted. Membership in a group
can be teacher-determined, selected by students, or random, and the groups can be
heterogeneous or homogeneous. There is some evidence supporting the claim that groups
that are heterogeneous in terms of participants’ gender, status, culture, or expertise are more
productive, even at low age, for collaborative learning (Cranton, 1998; Johnson &
Johnson, 1996; Webb & Palincsar, 1996). Such groups expose the learner to multiple
perspectives on issues and tasks based on the diverse backgrounds and experiences of the
other members of the group. Distributing minority or female students among groups to
achieve heterogeneity, however, can also result in isolation or students’ marginalization
(Felder, Felder, Mauney, Hamrin, & Dietz, 1995).
Community Ethos
The term community is described in various ways by researchers, but generally includes the
group members’ feelings of connectedness and commonality of learning expectations and
goals (Rovai, 2002). Although the context of collaborative learning has received relatively
little attention by researchers (Cockrell, Caplow, & Donaldson, 2000), it is recognized that
the classroom context, ethos, or culture may impact learning (Brown & Duguid, 2000;
Dede, 1996). It is particularly important to understand the way the affordances for
combining onsite and online interaction offer new environments and possibilities for
collaborativ e learning (Jones, Sca nlon, Blake, 2000; Littleton & Whitelock, 2005;
Warschauer, 1997). Distributed communities are also coming of age (Scardamalia, 2002;
Stone-Wiske, 2002); however, a challenge confronting them is how to increase the social
presence of the instructor and the learners. Social presence is defined as the ability of
learners to project themselves socially and affectively into a community of inquiry (Rourke,
Anderson, Garrison, & Archer, 2001).
Teacher-Student/Student-Student Online Interaction
In an analysis of 145 experiments using synchronous and asynchronous communication as
an independent variable over a twenty-year span of research, Fjermestad (2004) observed a
29.2% effect of group support systems (GSS) over face-to-face methods, and goes on to
suggest that the use of a GSS improves decision quality, depth of analysis, equality of
participation, and satisfaction more effectively than face-to-face methods. However, online
interaction is sensit ive to the ways the teacher plans, structures, and supports the
interaction.
Task Structuring
Relevant to the issues of collaborative vs. cooperative learning is the extent to which the
learning environment, roles, and tasks are structured for the learner. Rules and scripts
determine the level of task structure (Bernard et al., 2004; Lou, Abrami, & d’Apollonia,
2001). A script is “a story or scenario that the students and tutors play as actors play a
movie script” (Dillenbourg, 2002, p. 11). Strijbos, Martens, and Jochems (2004) suggested
that prescribed functional roles in instruction appear to affect the perceived level of group
72 Educ Psychol Rev (2007) 19:65–83

efficiency. Some research results show that structuring is required to avoid information
overload (Lim & Liu, 2006), and that too much scripting leads to less interaction.
Co-constructivist forms of scripting, like problem-based learning and project-based
learning, have been associated with positive learning outcomes (Blumenfeld, Marx,
Soloway, & Krajcik, 1996; Duisburg & Hoope, 1999; McManus & Aiken, 1995; Pearson,
2006; Rummel & Spada, 2005; Steinkuehler, Derry, Hmelo-Silver, & DelMacelle, 2002).
The ability of participants to see what they build together, i.e., group visualization (Pea,
1994), is also another form of structure that has been effectively applied to the learning of
scientific concepts and phenomena. It is also reflected in the use of shared online
whiteboards and other tools that enable students to interact in real time.
Scaffolding and Group Leadership
There is a consensus among researchers on the importance of the instructor’s leadership
role and behavior in online collaborative learning in supporting group learning processes
(Pea, 2004; Wallace, 2003; Weinberger, Fischer, & Mandl, 2002). Online interaction does
not evolve towards higher levels of discussion without proper grounding, monitoring,
modeling, coaching, or contributing on the part of the instructor, particularly at the onset of
instruction (Brandon & Hollingshead, 1999; Hiltz, Dufner, Holmes, & Poole, 1991). It is
also important to create a “joint problem space” (Teasley & Roschelle, 1993) and
establishing jointly agreed upon goals (Chan, Burtis, & Bereiter, 1997).
Meaning-Making
For Dillenbourg, Baker, Blaye, and O’Malley (1996), meaning-making refers to the
meaning of utterances during negotiation of a learning task or object of knowledge that
allows for different views, ideas, and opinions to be formulated and contributes to group
intersubjective understanding (Koschmann, 2002). It is facilitated by the process of
collaborative construction of knowledge through social negotiation (Jonassen, 1994).
Meaning-making, as a critical element of learning, involves active participation (Lave &
Wenger, 1991) in a networked community (Barab, Kling, & Gray, 2004; Schlager & Fusco,
2004). Grounding is often an issue, and the basic requirement is that learners add to their
common ground in an orderly way by trying to establish for each utterance the mutual
belief that all have understood what the speaker meant (Clark & Schaefer, 1989). Roschelle
(1992) asserts that the crux of collaboration is convergence, and analyzes collaboration as a
process that gradually leads to a convergence of meaning between two or more people.
Students’ representations of authority, however, may lead them to converge too early in
developing shared meaning (Hübscher-Younger & Narayanan, 2003).
An essential condition for meaning-making is that students must actively engage in
meaningful discourse related to the learning task or issue. Student participation in
discourse, however, can sometimes represent a challenge for instructors. Gunawardena
et al. (1997), for example, found low levels of discourse among students using a five-level
model of co-construction of knowledge. For higher levels of discourse to be observed, and
providing that the above factors are taken into consideration, a whole new epistemology
and its own suite of tools may be necessary.
Educ Psychol Rev (2007) 19:65–83 73

Collaborative Knowledge Building
From the knowledge building perspective, the notion of task is replaced by that of
intentional goals (Bereiter & Scardamalia, 1989) and idea improvement (Scardamalia &
Bereiter, 2003). Scardamalia and Bereiter defined k nowledge building “as the production
and continual improvement of ideas of value to a community, through means that increase
the likelihood that what the community accomplishes w ill be greater than the sum of
individual contributions and part of broader cultural efforts” (2003, p. 1371). Hakkarainen
(2006) suggests a trialogical approach to working with knowledge, one in which
knowledge building is a knowledge practice distinct from acquisition and participation
practices (Sfard, 1998). At the higher ed ucation level, critical factors in the use of
technology in support of collaborative efforts for knowledge advancement have been
identified: student engagement (Brett, 2004); teacher scaffolding for the development of
an explanation-orientation in the students’ discourse (Lipponen, 2000); pedagogical
strategies to tr ansform a traditi onal classroom into a knowledge building community
(Hewitt, 2002); and peer scaffolding ( Lai & Law, 2005). Stahl (2006) also emphasizes
quality grou p interaction for knowledge building purposes.
When engag ing in knowled ge building, participants seek deep understanding of
knowledge objects and are encouraged to create artifacts of value to others through a
process of idea improvement, be they the members of an on-campus community or an off-
campus community.
Time Requirements
Teaching time required for facilitation, moderation, or scaffolding is well acknowledged.
While technology affordances (e.g., scaffolds, visual computation of group activity, or
representations of group thinking) may be helpful supports, social affordances (teacher,
peers, broader context) remain key. Some examples of the many unanswered questions are:
How much of the sc affolding responsibility can be transferred to students (peer
scaffolding)? What are the ways in which the learning artifacts of one cohort may be
used as mediation tools by an upcoming cohort? Cumulative case studies would provide
insights into the time required for designing, monitoring, supporting, and assessing learning
in online collaborative environments and provide rich descriptions of faculty experiences,
benefits, and challenges in using CSCL.
Research in the above areas may help refine our knowledge about what technology
supported collaborative learning shares with face-to-face collaborative learning and what is
unique to each environment.
Recommendation 4. Research is needed on student characteristics, particularly of the
neo-millennial students now entering higher education and for whom connectivity and
communication via technology (e.g., IMing, Blogs, personal web pages, Wikis) is a
major part of their lives outside of the classroom. Research is needed to determine
whether these students will more likely embrace CSCL, or whether they will feel
unnecessarily constrained by the affordances of the current CSCL environments and,
if so, what elements will need to change.
74 Educ Psychol Rev (2007) 19:65–83

The Net Generation (N-Geners, Tapscott, 1998; McCain & Jukes, 2001) is now in
colleges and universities. Technology is more transparent to them than to previous
generation because digital technology is a part of their lives (cell phones, Nintendo and
PlayStation, PS2, MySpace, YouTube). Tapscott (1998)arguedthatthishascreateda
generation of students who have become independent, inclusive, and innovative.
Nonetheless, Wallace, in his review of online interaction between higher education
instructors and students, found that student engagement with cognitively complex ideas
is not common (2003). Personal factors such as students’ prior knowledge, metacog-
nitive and collaborative skills, as well as contextual cues such as cultural compatibilit y
(Francescato et al., 2006 ; Reeder, Macfadyen, Roche, & Chase, 2004;vanAalst&
Chan, 2001) and instructional methods (e.g., teacher scaffolding) influence student
engagement.
Student prior knowledge is known as the most important variable determining the
quality of students’ contributions both in online and face-to -face environments. Wilson
(2000) noted that successfu l, high achieving, high aptitude students present the same
characteristics whether they are engaged in onsite or online interaction. Moreover, female
high aptitude, and “sensing-making” (as opposed to “intuitive-feeling”)studentsmake
more contributions. Personality types and preferred learning strategies are also related to
student performance in online or onsite collaboration (Wilson, 2000). There are other
variab les that may affect student performance i n CSC L including the st udent’s attitudes
towards, and competence in using, technology. For example, lack of keyboarding skills or
ignorance of more advanced functionalities can limit a student’s participation in live
chats. In addition, students may not understand the benefits of online collaboration or
have had prior experience in working co llaboratively. Lockyer et al. (2001)recommends
that learners be supported in their development of group process sk ills (e.g., interaction
modelling).
Recommendation 5. More research is needed on the design elements of CSCL tool
software to determine the extent to which they support, structure, regulate, facilitate
or constrain the interactions of teachers and students (Strijbos et al., 2004).
Generic tools such as e-mail, file attachments, electronic bulletin boards, chat, blogs,
wikis, digital audio and videoconferencing systems, asynchronous/synchronous communi-
cation tools of Web-based Instructional Management Systems (Course Management
System, CMS; Learning Management System, LMS), and virtual learning environments
(Blackboard/WebCT, Moodle, Sakai, Claroline, FirstClass) are not only widely used for
business or educational delivery of information purposes, but are also used to support
online collaboration. Th ere are an increasing numbe r of tools and o nline environments
emerging that are especially designed with affordances to support collaborative learning
or kno wledge building. For in stance, the d atabase with embedded tools in Knowledge
Forum enables learners to engage in intentional learning and high-level processes of
collaborative inquiry through progressive discourse (Scardamalia & Bereiter, 1994).
Initially, the database is empty, a nd students use, within certain constraints, the tools to
collectively improve their ideas. Other advanced CSCL environments have capabilities to
support specific collaborative purposes. For example, TAPPED_IN offers numerous
virtual rooms for distributed communities to communicate synchronously (Schlager &
Schank, 1997). Belvedere was designed for collaborative learning through inquiry
Educ Psychol Rev (2007) 19:65–83 75

diagrams (Suthers, Weiner, Connelly, & Paolucci, 1995); CoVis used collaborative
visualization for cooperative project work in high-school science (Pea, Edelson, &
Gomez, 1994); and CoWeb is a collaborative hypertext environment that enables anyone
to create or edit Web pages (Rick & Gudzial, 2006).
In spite of the increasing availability of platforms and tools designed to support
collaborative learning, advanced technologies to support online collaboration are still in
early stages of adoption in campus-based or distance courses. In contrast, Internet browsers
have rapidly become effective for transmitting or accessing information for administrators,
teachers, and students, but provide limited support for the individual and group
understanding that drives collaboration. Gibson’s(1979) ecological approach to perception
is instructive here. He defined an affordance as the possibility-to-act in a given
environment. Gaver (1996) argued that a relation needs to be established between the
designer’s and the user’s intentions. For instance, a teacher may select an electronic
conferencing system for its features that support progressive discourse (Scardamalia &
Bereiter, 1996), negotiation (Lim, 2003), or argumentation (Andriessen et al., 2003;
McAllister, Ravenscroft, & Scanlon, 2004). The students, however, may not perceive or
understand the use of the system’s features, thereby preventing or limiting the desired
facilitation and support of peer-to-peer online interactions (Ferdig, 2006; Murphy, 2004;
Veerman & Treasure-Jones, 1999).
In its early years, CSCL research focused on the use of technology as a mediational
tool within collaborative methods of instruction (Koschmann, 1996). Soon, r esearchers
found that teachers and students seldom used technology in t he classroom as intended by
designers, and they began to focus on online social interaction (Stahl, 2006). Research is
needed to better understand the ecologies of online collaborative learning and the types of
tools and affordances that m ay best support and enhance the process.
Recommendation 6. Research is needed on the organizational issues related to
implementing CSCL in higher education to determine the essential conditions that
must be in place for effective faculty use of CSCL (with particular attention to the level
of support provided).
The organizational issues related to the use of CSCL in higher education need to be
better understood. Higher education policies and organizational structures have evolved
over many years to support the traditional paradigm of teaching and learning and create
obstacles for faculty who wishes to incorporate pedagogical strategies such as CSCL. There
is, however, growing recognition of the need to change learning methods and models in
higher education to prepare students with the skills they will need to be competitive in a
rapidly changing, knowledge-based global society. For example, the Business-Higher
Education Forum (2003) in their report, Building a Nation of Learners: The Need for
Changes in Teaching and Learning to Meet Global Challenges, underscores the need for
developing new leadership and vision to redesign learning in our colleges and
universities. To achieve this goal, universities will need to understand the role that the
latest technology advancements can play in providing more effective learning e nviron-
ments. They must also provide high quali ty faculty development, tech nology resources ,
infrastructure, software tools, and technical support.
As noted earlier, little attention has focused on the educational design of CSCL envi-
ronments (Strijbos, Martens, Jochems, & Broers, 2004). There is also a tendency to focus
on a limited number of approaches, even though different learning tasks require different
environments, support structures, and technological tools. Lipponen et al. (2004) emphasize
76 Educ Psychol Rev (2007) 19:65–83

that the design of CSCL settings should consider the relationships between the learning
framework and the goal of collaboration as well as the technological tools and instructional
approach. Similarly, Kirschner et al. (2004) advocate the concept of multiple collaborative
environments, in which the design of the CSCL environment is shaped by decisions about
educational, technological, and social affordances of the specific learning task.
Research is needed on the adoption of CSCL as an educational innovation within higher
education in real-world settings. Such research will help to identify both the barriers and
facilitators to the use of CSCL as well as other socio-constructivist approaches to learning.
It will also provide a better understanding of the circumstances of use or conditions related
to learning and knowledge building outcomes in higher education settings, and lead to the
development of viable designs for adoption strategies within organizations. (See Fishman,
Marx, Blumenfeld, Krajcik, & Soloway, 2004; Rick & Gudzial, 2006; Jones et al., 2006;
Lonchamp, 2006).
Conclusion
Many perspectives contribute to the understanding of technology in support of collaborative
learning. The last 20 years have been highly productive for CSCL. The advances of the
learning sciences, combined with the needs of the knowledge society, have heightened the
requirements for flexible (time and spac e) and challenging (problem-solving and
knowledge building) learning environments. New analytical frameworks, derived from a
number of theoretical perspectives (e.g., activity theory), offer new directions for research
on collaborative learning. It may also be useful to consider the framework of presage,
process, and product variables in developing future studies. Research is needed to better
understand presage variables such as student characteristics and the technology affordances
that enhance or constrain collaborative learning. A better understanding is also needed of
critical processes or mediating variables, such as task-structuring, well or ill-defined
problems, student engagement, teacher scaffolding, and the ways they combine to create
online written discourse. Lastly, research should focus on the product variables that are
claimed to be the most important outcomes of collaboration, such as higher-order thinking,
deep understanding, and knowledge creation.
Critical to the advancement of collaborative learning is its theoretical-empirical basis,
and a growing number of researchers posit both new possibilities and challenges to its
continuing development. Yet, the extent to which instructors will choose to engage students
in collaborative learning remains a moral issue (Goodlad, Soder, & Sirotnik, 1990), one
grounded in each instructor’s own beliefs about teaching.
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