ArticlePDF Available

Abstract and Figures

There is a wealth of research on computer-supported cooperative work (CSCW) that is neglected in computer-supported collaborative learning (CSCL) research. CSCW research is concerned with contextual factors, however, that may strongly influence collaborative learning processes as well, such as task characteristics, team formation, team members? abilities and characteristics, and role assignment within a team. Building on a critical analysis of the degree to which research on CSCW translates to CSCL, this article discusses the mediating variables of teamwork processes and the dynamics of learning-teams. Based on work-team effectiveness models, it presents a framework with key variables mediating learning-team effectiveness in either face-to-face or online settings within the perspective of learning-team development.
Content may be subject to copyright.
This article was downloaded by: [Jos Fransen]
On: 27 January 2013, At: 08:48
Publisher: Routledge
Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer
House, 37-41 Mortimer Street, London W1T 3JH, UK
Educational Psychologist
Publication details, including instructions for authors and subscription information:
http://www.tandfonline.com/loi/hedp20
Team Effectiveness and Team Development in CSCL
Jos Fransen
a
, Armin Weinberger
b
& Paul A. Kirschner
c
a
Department of Education/Centre for eLearning, Inholland University of Applied
Sciences, The Netherlands
b
Educational Technology, Saarland University, Germany
c
Centre for Learning Sciences and Technologies, Open University of The Netherlands
To cite this article: Jos Fransen , Armin Weinberger & Paul A. Kirschner (2013): Team Effectiveness and Team
Development in CSCL, Educational Psychologist, 48:1, 9-24
To link to this article: http://dx.doi.org/10.1080/00461520.2012.747947
PLEASE SCROLL DOWN FOR ARTICLE
Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions
This article may be used for research, teaching, and private study purposes. Any substantial or systematic
reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to
anyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representation that the contents
will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses
should be independently verified with primary sources. The publisher shall not be liable for any loss, actions,
claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or
indirectly in connection with or arising out of the use of this material.
EDUCATIONAL PSYCHOLOGIST, 48(1), 9–24, 2013
Copyright
C
Division 15, American Psychological Association
ISSN: 0046-1520 print / 1532-6985 online
DOI: 10.1080/00461520.2012.747947
Team Effectiveness and Team Development
in CSCL
Jos Fransen
Department of Education/Centre for eLearning
Inholland University of Applied Sciences, The Netherlands
Armin Weinberger
Educational Technology
Saarland University, Germany
Paul A. Kirschner
Centre for Learning Sciences and Technologies
Open University of The Netherlands
There is a wealth of research on computer-supported cooperative work (CSCW) that is ne-
glected in computer-supported collaborative learning (CSCL) research. CSCW research is
concerned with contextual factors, however, that may strongly influence collaborative learning
processes as well, such as task characteristics, team formation, team members’ abilities and
characteristics, and role assignment within a team. Building on a critical analysis of the degree
to which research on CSCW translates to CSCL, this article discusses the mediating variables
of teamwork processes and the dynamics of learning-teams. Based on work-team effectiveness
models, it presents a framework with key variables mediating learning-team effectiveness in
either face-to-face or online settings within the perspective of learning-team development.
The social-constructivist paradigm holds that collaborative
learners should be involved in processes of knowledge con-
struction to achieve deep learning and conceptual change
through discussion and argumentation (Bereiter, 2002;
Bruffee, 1993; Salomon & Globerson, 1989). Learning-
teams are effective to the extent that learners intend to and
actually manage to achieve these learning goals. Their goal
is to learn while working on a problem, project, task, and
so forth. Work-teams, on the other hand, are effective when
they successfully use their distributed expertise to effectively
and efficiently perform as a team to successfully complete
a given task. In work-teams, learning may occur as a by-
product of this collaboration, but it is not the primary goal,
though many employers see this as an added value of work-
ing in teams (Kayes, Kayes, & Kolb, 2005; Sessa & London,
2007).
Correspondence should be addressed to Jos Fransen, Department of
Education/Centre for eLearning, Inholland University of Applied Sci-
ences, Posthumalaan 90, 3072AG Rotterdam, The Netherlands. E-mail:
jos.fransen@inholland.nl
Because learning (i.e., knowledge construction) is the
primary goal of learning-teams in educational settings,
even if the assigned task is to complete a product, team
effectiveness i s primarily defined in terms of t he quality
of team learning and individual learning, whereas team
effectiveness in work-teams is primarily about product
quality. This implies that variables mediating learning-team
effectiveness can, and maybe even should differ from
variables mediating work-team effectiveness, or variables
mediating effectiveness in both contexts may differ in
their impact. This article first critically analyzes research
on work-team effectiveness in organizational settings to
establish to what extent the wealth of work-team research
may inform research on learning-team effectiveness and
then presents a conceptual framework on learning-team
effectiveness for research on collaborative learning.
The differences between work-team effectiveness and
learning-team effectiveness are mirrored by differences in
the focus of research on team effectiveness in both contexts.
Research on work-teams in organizational settings considers
multiple aspects of work-team effectiveness such as speed,
Downloaded by [Jos Fransen] at 08:48 27 January 2013
10 FRANSEN, WEINBERGER, KIRSCHNER
performance, accuracy, and inventiveness, as well as atti-
tudinal and behavioral indicators within the input-process-
output perspective (Bachmann, 2006). Most of this research
is related to long-term production teams or task groups in
organizations with a focus on task-specific teamwork, as-
pects of team leadership, relations between teams and their
organizations, and effects of environmental characteristics on
team effectiveness (Cohen & Bailey, 1997; Hackman, 1990;
Halfhill, Sundstrom, Lahner, Calderone, & Nielsen, 2005;
Stewart & Barrick, 2000). Effective teams are defined by
these researchers in terms of quality of the outcomes with
respect to organizational standards, and satisfaction of team
member’s needs.
Studies on the effectiveness of learning-teams (i.e., re-
search on collaborative learning) in educational settings often
focus on processes and outcomes quite different from studies
on work-teams, and they also partly define team effectiveness
in terms of the engagement of team members in the learning
task (Barron, 2003; Salomon & Globerson, 1989; Wang &
Lin, 2007). Collaborative learning research focuses on pa-
rameters influencing mindful engagement and collaboration
such as learning styles and/or cognitive ability (Alfonseca,
Carro, Mart
´
ın, Ortigosa, & Paredes, 2006; Webb & Palincsar,
1996), decision-making styles and group interaction
(Hirokawa, Cathcart, Samovar, & Henman, 2003), lead-
ership and/or role assignment in learning-teams (Johnson,
Suriya, Won Yoon, Berrett, & La Fleur, 2002; Strijbos,
Martens, Jochems, & Broers, 2004), and the dynamics of
specific kinds of learning-teams such as virtual teams (John-
son et al., 2002; Yoon, 2006). Also, research on collaborative
learning doesn’t focus only on parameters influencing collab-
orative learning outcomes. Recent research has increasingly
turned its focus to the processes that take place—both in
individual learners and in the team—during collaborative
learning (Dillenbourg, Baker, Blaye, & O’Malley, 1996;
Strijbos & Fischer, 2007; Weinberger, Stegmann, & Fischer,
2007). This implies a shift from determining the conditions
under which students effectively collaborate such as group
composition, individual prerequisites, and task features (i.e.,
the conditions paradigm) to determining the interactions
that occur under, the conditions under which they occur,
and what the effects of these interactions are (i.e., the
interactions paradigm; Dillenbourg et al., 1996). Finally,
there has been much research focusing on the learners’
cognitive progress during collaborative learning as well as
on the socioemotional processes that occur (e.g., creation
and maintenance of social relationships and a sense of
community; Kreijns, Kirschner, & Jochems, 2003).
Although it may be assumed that learning-teams in
project-based learning or case-based learning encounter
similar challenges and constraints in developing team
cohesion and effective collaboration as ad hoc project teams
in organizations (Chiocchio & Essiembre, 2009), learning-
teams and work-teams typically differ regarding both the
distribution of expertise within the team (Furst, Blackburn,
& Rosen, 1999; Weinberger et al., 2007) and the functional
hierarchy and team leadership within the team (Katz, Lazer,
Arrow, & Contractor, 2004). This means that whereas
work-teams are often formed with the explicit intention of
combining different types of expertise and usually include
a designated team leader with team-leader status, learning-
teams typically often contain no experts (i.e., they are all
learners) or designated leaders with such status in advance.
However, both learning-teams and work-teams must develop
as a team to become effective, which means that the relevance
and impact of variables mediating team effectiveness should
be discussed within the perspective of group development.
It is, therefore, interesting and potentially fruitful to explore
the variables mediating team effectiveness in both educa-
tional and organizational settings to establish prospective
similarities and differences on the effects of these variables
in both contexts. We thus critically analyze the extent to
which research on team effectiveness in computer-supported
cooperative work (CSCW) can contribute to our under-
standing of research in computer-supported collaborative
learning (CSCL) to develop a conceptual framework on team
effectiveness and development in CSCL. To this end, we first
present a model of group development to be applied in the
context of collaborative learning. Then we review research
findings from work settings and contrast them with findings
from learning settings. Finally, we discuss a learning-team
effectiveness framework including those variables mediating
team effectiveness in collaborative learning settings within
the perspective of learning-team development.
MODELS OF GROUP AND TEAM
DEVELOPMENT
Teams, and especially ad hoc learning-teams, are often
initially ineffective because team members lack necessary
information about one another’s competences and do not
exhibit mutual trust, having not experienced one another’s
behavior in a team situation (Lewicki & Bunker, 1996). This
can be complicated by the fact that students often are ran-
domly assigned to learning-teams or students are reassigned
to a different learning-team during a course or a semester,
which result in groups with some students sharing collabora-
tion experiences from previous tasks and others collaborating
for the first time (Janssen, Erkens, Kirschner, & Kanselaar,
2009). Such ad hoc groups experience development—often
described as different developmental stages—in which the
influence of different variables mediating learning-team
effectiveness may vary. For instance, groups with high group-
member familiarity (i.e., group members share collaboration
experiences from previous tasks and/or because they are
friends) develop more critical and exploratory group norms,
which leads to more efficient communication and spending
less time in monitoring task-related activities (Janssen et al.,
2009), which results in the group proceeding through the
developmental stages more quickly and/or in a manner
different from groups with low group-member familiarity
Downloaded by [Jos Fransen] at 08:48 27 January 2013
TEAM EFFECTIVENESS AND TEAM DEVELOPMENT IN CSCL 11
(i.e., so-called zero-history groups). These stages have been
described in different models of group development that can
be applied in the context of collaborative learning.
Group development models, originally developed to ex-
plain group development in organizational settings, can be
divided into three major categories: linear progressive mod-
els, cyclical models, and nonsequential models (Mennecke,
Hoffer, & Wynee, 1992). Because these models were not
developed to explain group development in educational set-
tings, their use there must be critically examined. Linear pro-
gressive models suggest that groups progressively develop in
a specific direction, maturing over time. Such models imply
a set of stages in a more or less strict order. The Tuckman and
Jensen model (1977) is the best known example of a linear
progressive model. This widely used model for small group
development distinguishes five stages of group development:
forming (i.e., getting to know one another and the task at
hand), storming (i.e., establishing positions on the task and
roles within the group), norming (i.e., reaching consensus
about group norms, goals, and strategies), performing (i.e.,
reaching conclusions and delivering results), and adjourning
(i.e., dismantling the group; reevaluation of team goals with
respect to personal goals). Although groups may differ in
many aspects and stages are not always linear or visited only
once, the Tuckman and Jensen model has been successfully
tested for decades in different contexts. Because students
often are assigned randomly to ad hoc learning-teams and
members need to get to know one another, it is likely that the
developmental process will be progressive to some extent
though, depending on the length of the task, sometimes not
achieving all stages.
Cyclical models also claim that groups develop by pro-
ceeding through stages but hold that groups continually re-
visit stages during the developmental process. They have to
deal with similar issues and problems at different moments as
a result of environmental changes, changes within the group,
or changes in the task at hand. Progression in cyclical models
means that a g roup matures during smaller or shorter devel-
opmental cycles and that it will flexibly modify its approach
to dealing with the same issues over time based upon its prior
experiences. A specific ordering of developmental stages is
not necessary, although groups eventually find a workable
solution for achieving its objectives, after which it has to be
determined if the group continues or disbands (Smith, 2001).
Cyclical models acknowledge the fact that groups have to be
flexible to deal with environmental demands and constraints
and these models appear to be more capable of explaining
group development in the real world by addressing a group’s
ability to assess new information and adjust their teamwork
strategy (Smith, 2001). Cyclical models may be only partly
applicable in some long-term educational contexts, because
for many short-term learning-teams changes in the environ-
ment and changes in the task are less likely to happen. In
addition, whereas cyclical models of CSCW apply to teams
that work together over multiple tasks, in CSCL team compo-
sition is typically ad hoc and changes after task completion,
assignments are hardly ever repeated but typically adjusted
to advancing levels of competence, and students are usually
assigned to a new team for each new assignment. This, how-
ever, does not mean that past experiences with teamwork in
ad hoc learning groups does not influence a student’s ex-
pectations of teamwork. Research has shown that students
entering a new team are affected by their prior experiences
in teams with either similar or different backgrounds which
affects the developmental pattern of the team (Hinsz, 1995;
Rentsch, Heffner, & Duffy, 1994). Although team composi-
tion may change with a new assignment, students probably
collaborated with other classmates in different team forma-
tions, resulting in teamwork mental models becoming stable
and groups visiting developmental stages less often or more
quickly proceeding through specific stages.
In nonsequential models, patterns of development are
largely the result of environmental factors such as time con-
straints and task characteristics. Given the task at hand and
the existence of deadlines for delivering results, solving these
task-related problems will be of more influence on group
development than the dynamics of inter personal relations
(Gersick, 1988). In Gersick’s punctuated equilibrium model
group development is not slow and progressive, but rapid
and abrupt. For example, groups working on a task with a
clear deadline need time to explore the task before starting
to produce results. More or less halfway to the deadline, they
experience a turning point. Groups that have not performed
well will now start performing in order to deliver results
on time. Groups that have performed well (i.e., already pro-
ducing the required results) tend to change this performance
toward more practical and goal-oriented performance. Dead-
lines and the task at hand will influence group development,
and relations between members will change after passing
through the tur ning point. Given the fact that students tend
to act pragmatically and to balance the investment of time
and effort between teamwork and other activities (Fransen,
Kirschner, & Erkens, 2011), the nonsequential models seem
to be applicable to some extent and acknowledge the behav-
ior of shor t-term learning-teams, especially the punctuated
equilibrium model (Gersick, 1988). However, research with
learning-teams has also shown that the more effectively a
team operates, the more likely this team follows a linear
progressive development (Johnson, Surya, Yoon, Berrett, &
La Fleur, 2002). Although an ad hoc student learning-team
probably follows a progressive developmental path, as it usu-
ally is a mixed group with students having already worked
together on previous tasks and students working together
for the first time, students still tend to operate pragmatically
and economically invest the available time, which implies
the importance they place on solving task-related problems
and delivering results on time (Chinn, O’Donnell, & Jinks,
2000).
The Team Evolution And Maturation model or TEAM
model (Morgan, Salas, & Glickman, 2001) combines existing
Downloaded by [Jos Fransen] at 08:48 27 January 2013
12 FRANSEN, WEINBERGER, KIRSCHNER
Brief explanation of task-related skills and team-related skills in the different phases of team development
First meeting
Phase 1
Transition
Phase 2
Completion
Groups are composed and group members explore the task characteristics and the characteristics
of each-other to establish task complexity and their interdependency for completing the task.
Group members responding differently to the task and to group members claiming roles, reaching
consensus on strategy, roles and group norms through open exchange, and starting to perform.
Adjusting goals and/or strategy as well as re-allocating subtasks and/or re-dividing roles based on
reflecting on the quality of the group member’s individual contributions to the overall
Group members effectively contributing to completion of the task by fulfilling their roles as agreed
upon in the transition phase, resulting in speeding up performance and delivering results.
Group members completing the task and delivering results, respecting preset deadlines while at
the same time adjusting teamwork in order to deal with changing environmental demands.
FIGURE 1 Essentials of the TEAM model with teamwork phases, team development stages, and the convergence of task-related skills and team-related
skills during team maturation. From An Analysis of Team Evolution and Maturation, by B. Morgan, E. Salas, & A. Glickman, 2001, The Journal of
General Psychology, 120, p. 281. Copyright 2001 by Rockefeller University Press. Adapted with permission.
theories and ideas into a general team-development model,
including Tuckman’s stages model and Gersick’s punctuated
equilibrium model. The TEAM model describes a set of
developmental stages, but a team does not have to proceed
through all stages and may start at different stages, according
to past experiences of the team and its members. The model
also determines a task-oriented path and a team-oriented path
along which teams develop, respectively addressing task-
skills like reaching agreement on goals and strategies and
delivering results and team-skills like developing group co-
hesion on the basis of role division and interdependence. The
optimum level of performance is reached when the two paths
converge (see Figure 1).
The TEAM model seems appropriate for application in
the educational context because it acknowledges that ad hoc
learning-teams have to develop by proceeding through stages,
whereas it also acknowledges the importance of the effect of
deadlines on learning-team development, the emergence of
a transition phase (i.e., the re-norming stage), and the impact
of past experiences with teamwork on the pattern of team
development. The TEAM model offers a framework for dis-
cussing the variables mediating learning-team effectiveness,
assuming that the impact of these variables may differ accord-
ing to the stage of learning-team development and may have
a specific impact on learning-team evolution and matura-
tion. The variables mediating learning-team effectiveness are
Downloaded by [Jos Fransen] at 08:48 27 January 2013
TEAM EFFECTIVENESS AND TEAM DEVELOPMENT IN CSCL 13
explored in the next section, initiated by exploring a frame-
work based on the findings of a review of research on work-
team effectiveness.
VARIABLES MEDIATING TEAM
EFFECTIVENESS
The best known framework for teamwork—The Big Five
in Teamwork—is based on a meta-analysis of research on
team effectiveness in organizational settings (Salas, Sims,
& Burke, 2005). I t covers five key factors influencing team
effectiveness and three supporting and coordinating mech-
anisms. The Big Five factors are team orientation, team
leadership, mutual performance monitoring, back-up behav-
ior, and adaptability. The three supporting and coordinat-
ing mechanisms are shared mental models, mutual trust, and
closed-loop communication. These variables (i.e., factors and
mechanisms) mediating team effectiveness and their impor-
tance for the educational setting are explored, starting with
the supporting and coordinating mechanisms as conditional
for influencing the Big Five, followed by an exploration of
the Big Five. In spite of the popularity and dissemination
of the Big Five, the importance of these variables mediat-
ing team effectiveness for educational settings has not yet
been systematically investigated. By integ r ating these find-
ings, we aim to develop a coherent model of the variables
that mediate learning-team effectiveness within the perspec-
tive of learning-team development. We emphasize, however,
that the broad scope of research on team effectiveness and the
complexity of the constructs involved require us to gloss over
some of the more nuanced findings within each of the fol-
lowing sections and report only on a selection of the findings
for each of the topics we address.
Shared Mental Models
Findings from CSCW research.
Developing a shared
understanding in a team (i.e., compatible mental models of
the task that are sufficiently aligned so as to coordinate mul-
tiple task-related perspectives and efforts; Derry, DuRussel,
& O’Donnell, 1998) is conditional for setting team goals,
deciding on strategies, allocating subtasks to team members,
monitoring team processes adequately, and communicating
effectively (Klimoski & Mohammed, 1994; Van den Boss-
che, 2006). Team members develop these compatible mental
models in a process of negotiating and interrelating one
anothers’ diverse views (Akkerman et al., 2007). Different
researchers use different terms with respect to shared
understanding such as common ground (Beers, Boshuizen,
Kirschner, & Gijselaers, 2006), synergistic knowledge (Mu
& Gnyawali, 2003), team mental models (Mohammed &
Dumville, 2001), or shared mental models (Salas et al.,
2005; Stout, Cannon-Bowers, Salas, & Milanovich, 1999). A
distinction can be made between mental models that are t eam
related and those that are t ask related (Mathieu, Heffner,
Goodwin, Salas, & Cannon-Bowers, 2000). In team-related
mental models, the focus is on awareness of team functioning
and the expected behaviors of the team as a whole and the
team members individually. The focus in task-related mental
models is on information on the materials and strategies
needed to successfully carry out the team task.
Each team member’s mental model should be sufficiently
similar to those of other team members to guide the team as
a whole toward the team’s objectives, but these need not and
should not be exactly the same, because input from different
perspectives has been found to improve team decision
quality and performance (Kellermanns, Floyd, Pearson, &
Spencer, 2008). Also, teams have been found to benefit
from sharing transactive knowledge (i.e., knowledge about
other team members’ knowledge; Cannon-Bowers, Salas,
& Converse, 1993). Recent work-team research has found
the accuracy of the shared mental models (i.e., similarity of
team members’ mental models with a canonical or expert
model) to be a more beneficial influence on teamwork
processes and team performance than a similarity of team
members’ mental models (Smith-Jentsch, Cannon-Bowers,
Tannenbaum, & Salas, 2008), as team members may develop
highly similar mental models that prove to be ineffective to
structure the planning and monitoring of teamwork. With
regard to team performance and effectiveness, teams guide
their actions based on a shared mental model developed
through exchanging different perspectives and team mem-
bers becoming aware of mental model dissimilarity, but their
effectiveness will only increase if there is a convergence
within the team towards an accurate shared mental model of
teamwork (Smith-Jentsch et al., 2008).
In the process of developing (i.e., working) as a team, team
members continuously update their shared mental models.
Findings suggest that members of teams that engage in high-
quality planning in the early stages of teamwork form better
shared mental models of one another’s information needs
during teamwork and perform better (Stout et al., 1999), and
becoming better aware of one another’s expertise results in
improvement of team performance (Yoo & Kanawattanachai,
1993).
Implications for research on CSCL.
Collaborative
learning, defined as a coordinated, synchronous activity that
is the result of a continued attempt to construct and maintain a
shared conception of a problem ( Roschelle, 1992; Roschelle
& Teasly, 1995) implies a situation that can be characterized
as “collaborative” where learners have similar status and sim-
ilar levels of knowledge (Dillenbourg, 1999), although status
differences may exist, but these will be less and less official.
However, learning-teams like work-teams have to develop
team-related and task-related mental models in early stages
of collaboration to become productive and deliver results.
This is complicated by the fact that learning-teams tend to
focus primarily on task-related mental models so as to act
Downloaded by [Jos Fransen] at 08:48 27 January 2013
14 FRANSEN, WEINBERGER, KIRSCHNER
pragmatically and efficiently as an ad hoc short-term team
to complete assignments on time (Fransen et al., 2011). Stu-
dent learning-teams, however, differ from work-teams with
respect to their ability to develop an elaborate mental model
of the outcomes of collaboration because students are, by
definition, not experts and mental model dissimilarity be-
tween members of learning-teams is likely to be small, as a
meta-analysis exploring the correlations between team abil-
ity, team heterogeneity, and team performance in different
team types showed (Chiocchio & Essiembre, 2009). How
learners jointly perform and attain their goals may depend
on how much time and effort learning-teams invest at differ-
ent stages of teamwork in developing shared mental models
of the task, goals, and strategies, as well as the knowledge
and skills of the other team members. These shared mental
models will typically develop to some degree in the process
of learners working together. At the same time, task-related
shared mental models have been regarded as intended out-
comes of collaborative learning (Weinberger et al., 2007).
CSCL research has built on work-team research to analyze
collaborative learning at group level, focusing not only on
the individual learning outcomes but also on the extent to
which learners converge toward shared knowledge (Wein-
berger et al., 2007). This work stresses how learning-teams
may particularly benefit from initial knowledge divergence
so that they use each other as complementary learning re-
sources and challenge their own ideas. Ultimately, learners
may then converge on a larger body of knowledge that they
have shared during collaboration.
Mutual Trust
Findings from CSCW research.
Mutual trust implies
the shared perception that every individual in the team will
perform particular actions important to its members and will
protect the rights and interests of all team members (Salas
et al., 2005). Without sufficient mutual trust, team members
spend too much time and energy protecting, checking, and
inspecting one another and one another’s’ behaviors, and too
little time constructively collaborating (Peterson & Behfar,
2003).
Research has shown that trust is a multidimensional con-
struct, differentiable from concepts such as cognition-based
trust versus affect-based trust (i.e., based on knowledge about
team members vs. emotional bonds with others; McAllister,
1995). Research also showed the interrelatedness of trust
and friendship, and the effects of an integration of the two
(Lewicki & Bunker, 1996; Newell & Swan, 2000). Friend-
ship, which refers to what is labeled as companion-based
trust (i.e., based on judgments on goodwill or personal friend-
ships), is resilient and based on emotional bonds, as opposed
to competence-based trust (i.e., cognition-based trust), which
is fragile and based on the perception of ability of others to
perform as agreed. Furthermore, the development of trust
seems to proceed through stages building upon each other
starting with calculus-based trust (i.e., based on the expected
competences of other team members), followed by the emer-
gence of knowledge-based trust (i.e., based on the perceived
expertise of other team members, and finally in identification-
based trust (i.e., based on valuing other team members
regarding their expertise, their behavior, and as a person;
Lewicki & Bunker, 1996; Sheppard & Sherman, 1998).
One might assume that virtual teams experience more
difficulties in developing mutual trust as a consequence of
computer-mediated communication, implying the absence
of proximity and therefore of the presence of others, face-to-
face communication, shared social settings, and frequency of
spontaneous communication (Kiesler & Cummings, 2002).
However, research findings show that virtual teams can de-
velop the same levels of trust as face-to-face teams, but
that it takes more time to realize this (Jarvenpaa & Leidner,
1999). Virtual teams appear to develop cognition-based trust
more quickly than face-to-face teams (J. Wilson, Strauss, &
McEvily, 2006), and the presence of swift trust (i.e., based
on information of team members’ backgrounds) in an early
stage of teamwork is a predictor of high performance of
virtual teams (Kanawattanachai & Yoo, 2002). In addition,
team member dissimilarity in age, gender, grade, or culture
is negatively related to the development of swift trust in face-
to-face teams but not in virtual teams due to a reduction in the
salience of dissimilarity (Krebs, Hobman, & Bordia, 2006).
Implications for research on CSCL.
Research on
trust and its effects in learning-teams is sparse, which may be
due to the fact that ad hoc and short-lived learning-teams have
mostly been investigated (Fransen et al., 2011). However, be-
cause learning-teams usually have no influence on environ-
mental factors mediating their performance, learning-team
members depend strongly on each other to work on the task
(Chiocchio & Essiembre, 2009). This interdependency and
lack of power of ad hoc learning-teams to influence task
conditions and resources underlines the importance of es-
tablishing minimal levels of cognition-based trust in early
stages of teamwork to develop task cohesion (i.e., agree-
ment on goals and strategies; Fransen et al., 2011). Although
the focus in learning-teams lies in achieving cognition-based
trust (i.e., based on perceived ability of others), companion-
based trust (i.e., based on emotional bonds) may interfere
with that if students develop friendships as a result of be-
ing classmates for some time. This companion-based trust
may strengthen mutual trust in a learning-team, but it can
also lead to faultlines within the team as a result of someone
taking over a team member’s subtask for reasons of a more
personal character and not related to team goals (Molleman,
2005). It emphasizes the fact that learning-teams are social
systems, with social cognitions both affecting social inter-
actions and resulting from it which may lead to debilitating
effects (Salomon & Globerson, 1989), and the emergence of,
for instance, the “free-rider” effect, which refers to a team
Downloaded by [Jos Fransen] at 08:48 27 January 2013
TEAM EFFECTIVENESS AND TEAM DEVELOPMENT IN CSCL 15
member not investing the required effort assuming that other
team members will do (Kerr & Bruun, 1983) and/or status
differences within the team when the perceived high ability
of one member leads to this member dominating the group
activities and receiving more help ( Dembo & McAuliffe,
1987).
Closed-Loop Communication
Findings from CSCW research.
Communication fa-
cilitates teams in updating their shared mental models and
engaging in activities regarding task execution, monitoring
the process, and adapting to changing conditions (Salas et al.,
2005). This increases in importance when the environment
increases in complexity, for instance, in operating room teams
(K. Wilson, Burke, Priest, & Salas, 2005), and implies com-
munication that is closed-loop in character. Closed-loop com-
munication consists of a team’s ability to exchange clear,
concise information, acknowledge the receipt of that infor-
mation, and confirm its correct understanding (P. Kirschner,
Beers, Boshuizen, & Gijselaers, 2008), as opposed to open-
loop communication, where the receipt of information is not
acknowledged and correct understanding is not confirmed
(Gillard & Johansen, 2004).
Team communication can also be characterized as either
centralized (i.e., the extent to which one member serves as a
hub of communication) or decentralized (Katz et al., 2004).
Centralized communication is adequate when the task is sim-
ple, but when it is complex, teams benefit from all members
participating in decentralized communication (Leavitt, 1951;
Shaw, 1954).
Implications for research on CSCL.
Research in
CSCL reveals that the communication of social and cognitive
information is a condition for effective team learning (Van
den Bossche, Gijselaars, Segers, & Kirschner, 2006), to
allow team members to establish a shared purpose through
knowledge of each other’s competences and create owner-
ship of the task (Tolmie & Boyle, 2000); to create a sense of
community, which is conditional for collaborative l earning
(Cho, Gay, Davidson, & Ingraffea, 2007; Wegerif, 1998);
and to develop an accurate understanding of differences in
prior knowledge within the t eam (Sangin, Molinari, N
¨
ussli,
& Dillenbourg, 2011). In CSCL research, different qualities
of communication pertinent to learning have been identified
such as argumentative quality of learners’ utterances
(Weinberger & Fischer, 2006). There are indications that
collaborative learners acquire better knowledge the more
they relate to what their learning partners are saying in
what has been termed transactive talk (i.e., operating on the
reasoning of another; Teasley, 1997).
An extra problem for CSCL is that CSCL teams typically
have to rely exclusively on computer mediated commu-
nication to exchange information within the team. Such
environments often lack the tools to allow for effective
closed-loop or transactive communication and/or the tools
that are available (e.g., chat, discussion boards), due to their
often linear and temporal character, do not adequately allow
for the reflection needed to achieve effective communication
(Lea, Rogers, & Postmes, 2002). One way to facilitate the
quality of learners’ interactions and especially transactive
talk in CSCL is to integrate sociocognitive structures into a
CSCL environment via collaboration scripts (Fischer, Kollar,
Mandl, & Haake, 2007; Weinberger, 2011). Such scripts
specify, sequence, and distribute roles and activities across
a group of learners and, thus, guide them to engage in trans-
active interactions. Weinberger, Ertl, Fischer, and Mandl
(2005) found that in a text-based as well as a video-based
CSCL environment, learners’ interaction could be scripted
toward becoming more transactive and that the group mem-
bers acquired more knowledge individually by distributing
and rotating the roles of analyst and critic in g r oups of three
and by prompting lear ners to peer review activities and ask
critical questions about their partners’ contribution to the
task. Even if learners do not follow a script perfectly, they
can increase the probability of transactive interactions and
closed-loop communication by asking learners to explicate
how they have understood their partners’ contributions.
Team Orientation
Findings from CSCW research.
Team orientation
refers to both a preference for working with others and a
tendency to enhance individual performance through coordi-
nation of one’s actions with other members while performing
group tasks (Salas et al., 2005). Team orientation facilitates
decision making, cooperation, and coordination among team
members, which in turn results in increased team perfor-
mance (Eby & Dobbins, 1997).
Team orientation is attitudinal and a result of team mem-
bers’ individual attitudes toward teamwork, and therefore de-
pends on a team’s composition. In work settings, this attitude
and its development can be enhanced by factors such as the
chosen reward structure (e.g., rewarding the team as a whole
for a good product vs. rewarding only the team leader), team
composition (e.g., team size and team characteristics match-
ing task demands), and single-team identity (e.g., belonging
to only one long-term team with mostly permanent members;
Campion, Papper, & Medsker, 1996).
Implications for research on CSCL.
Learning-teams
differ from work-teams in the sense that team members
are supposed to learn together, that assessment is often
individual, which may inhibit learning (Underwood, 2003),
and students tend to do what the teacher asks for. Nev-
ertheless, in educational contexts team orientation can
be influenced to some extent by teachers through team
formation procedures (e.g., random vs. planned for specific
Downloaded by [Jos Fransen] at 08:48 27 January 2013
16 FRANSEN, WEINBERGER, KIRSCHNER
pedagogical reasons), choice of assignments (e.g., open and
divergent vs. closed and convergent), and reward system
(e.g., assessment of individuals vs. assessment of the group
vs. both). Team formation based on learner characteristics
such as learning strategies have been shown to be ineffective,
whereas forming heterogeneous ability groups have been
shown to affect team orientation where low-ability students
and high-ability students perform equally well (Webb &
Palincsar, 1996). With respect to choice of assignments,
team orientation and learning have been affected most when
authentic, complex, and challenging assignments requiring
collaboration are used (Blumenfeld et al., 1991; F. Kirschner,
Paas, & Kirschner, 2009a, 2009b, 2011b). The effects of re-
ward systems and assessment strategies on peer learning vary
according to the choice of assessment in a given situation,
but assessment only positively influences team orientation if
assessment of the task and process are balanced, is focused
on both the individual and the group, and if students are
coresponsible through peer-assessment and self-assessment
(Boud, Cohen, & Sampson, 1999; Willcoxson, 2010).
Although this is the case, team orientation has often
been regarded as a learner trait (Driskell, Goodwin, Salas,
& O’Shea, 2006). Learners have been found to develop a
resistance to working in teams due to multiple negative ex-
periences in past collaborative learning experiences such as
increased time and work investment (i.e., increased transac-
tion costs), having to support and/or tolerate “social loafers”
and “free loaders, and a lower return on their time and work
investment (Hillyard, Gillespie, & Littig, 2010). The “lone
wolf phenomenon, referring to a learner’s preference to
work alone (Feldman Barr, Dixon, & Gassenheimer, 2005), or
silent participation (Remedios, Clarke, & Hawthorne, 2008)
may pose a greater problem in CSCL teams—as opposed
to face-to-face teams—due to the absence of nonverbal in-
formation mediated by being physical present in team meet-
ings to explain a team member’s behavior (Kerr & Bruun,
1983; Kiesler, Siegel, & McGuire, 1984; Lea, Spears, & De
Groot, 2001). In contrast, team orientation may be stimu-
lated through positive experiences with collaborative learn-
ing (e.g., feelings of increased group efficacy; F. Kirschner,
Paas, & Kirschner, 2011a). Also, the teacher can positively
influence orientation by offering a clear purpose and written
instructions, by matching team size to the pedagogical ob-
jectives, by maximizing team longevity, by giving students a
say in team assignments, by highlighting the value of each
members’ contributions, by implementing specific forms of
peer assessment such as peer rating, and by actively support-
ing team development and the process of teamwork (Bacon,
Stewart, & Silver, 1999; Felder & Brent, 2001).
Team Leadership
Findings from CSCW research.
Effects of team
leadership on team effectiveness have been widely studied
in different settings and contexts (Cummings & Cross,
2003; Ferrante, Green, & Forster, 2006; Hackman, 1990;
Nembhard & Edmondson, 2006). The effects of team
leadership also depend on the type of team and task at hand,
which means, for example, that long-term teams consisting
of members with specific expertise to execute subtasks
within the overall task have been found to benefit from
directive leadership, especially if a task implies execution
of specific subtasks in a strict order and/or addresses critical
or life-threatening situations (Hannah, Uhl-Bien, Avolio,
& Cavarretta, 2009). In contrast, a short-term team facing
problems that require new creative solutions will benefit
most from transformational leadership, aimed at encourag-
ing member autonomy and empowerment (Alimo-Metcalfe
& Alban-Metcalfe, 2001). A meta-analysis of research
on the relationship between team member satisfaction
and leadership style showed that teams prefer democratic
leadership instead of autocratic leadership, although the
effect on member satisfaction is moderated by team size, and
team composition (Foels, Driskell, Mullen, & Salas, 2000).
This characterization of types of leadership resembles the
distinction made between centralized leadership (i.e., one
acknowledged leader) and distributed leadership (i.e., every
team member is both a leader and a follower; Mehra, Smith,
Dixon, & Robertson, 2006). Among groups with distributed
leadership research has shown the importance of emergent
leadership (i.e., leadership that changes and emerges based
upon the need for the reinforcement, creation, and ongoing
evolution of team structures that guide the actions of team
members), especially in effective technology-supported self-
organizing groups (Heckman, Crowston, & Misiolek, 2007).
This emergent leadership refers to a shift in leadership from
distributed first-order leadership in early stages of teamwork
to a specific type of centralized second-order leadership in
later stages. First-order leadership focuses on reinforcing
existing structures through task coordination, substantive
task contribution, group maintenance, and boundary span-
ning, whereas second-order leadership aims at modifying
existing structures by influencing team member behavior to
improve task execution, reinforce cohesion, and deal with
environmental demands. This second-order leadership is
action embedded, which means that a team member only
gets the permission to lead after contributing substantively
to the team. First-order distributed leadership is conditional
for the acceptance of second-order centralized leadership,
as (a) team members have to agree on such centralization,
(b) the emergent second-order leader must already have
performed and demonstrated first-order leadership behavior,
and (c) a team must have developed accurate task-related
and team-related mental models (Heckman et al., 2007).
Implications for research on CSCL.
Learning-teams,
and especially virtual learning-teams, tend to benefit from
shared leadership for effective learning (Johnson et al., 2002),
provided that inequality in participation levels does not get
Downloaded by [Jos Fransen] at 08:48 27 January 2013
TEAM EFFECTIVENESS AND TEAM DEVELOPMENT IN CSCL 17
locked in early in the process of teamwork as a result of
dominant members’ proposals or contributions (Kapur,
Voiklis, & Kinzer, 2008). Teams relying too much on direc-
tive leadership tend to learn less through limited discussion
(Durham, Knight, & Locke, 1997). Also, learning-teams
usually have a short life cycle and are often supposed to
foster equal participation, which implies that team leadership
may be less influential for a learning-team’s effectiveness,
except when critical moments appear (e.g., in the case of
fast-approaching deadlines for submission of products).
Hogg, Abrams, Otten, and Hinkle ( 2004), for example, found
that in a critical situation, a team evaluates its performance
to that point in time and then adapt its strategies to deliver a
timely result. This adaptation includes redistribution of sub-
tasks and roles, often resulting in the emergence of a type of
centralized leadership where the most prototypical member
becomes the team leader because he or she is seen as the
personification of the ideal team player (Hogg et al., 2004).
In the sense that collaborative learning in general and CSCL
in particular has been regarded as means to foster equal
participation in learning processes, leadership in learner
groups has mainly been problematized in the few studies
on leadership in collaborative learning contexts (Garrison
& Cleveland-Innes, 2005; Johnson et al., 2002). However,
leadership in collaborative learning, and especially in
face-to-face collaborative groups, is difficult to differentiate
from helping behavior and should be placed on a continuum
of behavior of purely procedural to purely inspirational
within the perspective of collaborative reasoning (Miller,
Sun, Wu, & Anderson, in press). Leadership may emerge if a
team member has the necessary leadership competence (i.e.,
problem-solving skills, s ocial judgment skills, knowledge)
with leadership being primarily task oriented and procedural
(i.e., transactional leadership) or both task oriented and rela-
tionship oriented and therefore inspirational (i.e., motivating
and beneficial with respect to successfully solving the learn-
ing task together) similar to the emergence of second-order
leadership in distributed t eams (Heckman et al., 2007).
Mutual Performance Monitoring
Findings from CSCW research.
Mutual performance
monitoring implies keeping track of one’s fellow team mem-
bers’ work while carrying out one’s own work to ensure that
all is running as expected and procedures are followed cor-
rectly (Salas et al., 2005). The more complex a task (i.e., the
greater the number of elements and the higher the degree of
interactivity between those elements (P. Kirschner, 2002), the
more important mutual performance monitoring will be, and
when a task becomes stressful as a consequence of time con-
straints, mutual performance monitoring is conditional for a
team’s performance (Porter, Gogus, & Chien-Feng Yu, 2010).
Mutual performance monitoring requires a shared under-
standing of task and team responsibilities (i.e., a shared men-
tal model); otherwise, feedback becomes inconsequential and
monitoring is ineffective (Hsu, Chang, Klein, & Jiang, 2011;
Ying & Erping, 2010). Also important is trust, because only
in a trusting climate will members react positively to feed-
back of others (Peterson & Behfar, 2003).
Implications for research on CSCL.
Research in ed-
ucational contexts is less focused on mutual performance
monitoring for keeping track of one anothers’ work but more
on how mutual performance monitoring influences interac-
tion between learners and the learning process (Dillenbourg
et al., 1996; Wecker & Fischer, 2011). Wecker and Fischer
(2011) applied and faded a collaboration script in a text-
based CSCL environment to f acilitate learners’ argumenta-
tion by prompting learners, for instance, to provide counter-
arguments, to warrant, and to qualify their claims. To avoid
learners falling back to low-quality argumentation after the
script has been faded, peers were instructed to continuously
monitor each other’s argumentative moves. Fading scripts in
combination with peer monitoring facilitated levels of self-
regulation and knowledge on how to argue. Although re-
search on role assignment within a team (i.e., by a tutor or the
team itself) is still limited, research on assigned or acquired
roles has been shown to affect perceived team efficiency
by increasing awareness of group interaction and collabora-
tion (Weinberger, 2011). Therefore, role assignment within
learning-teams may facilitate and support effective mutual
performance monitoring because students in role groups con-
tribute more task-specific and coordination-focused state-
ments (Schellens, Van Keer, De Wever, & Valcke, 2007;
Strijbos, Martens, Jochems, & Broers, 2007). Also, balanced
teams with respect to role distrib ution show more efficient
and effective interaction than nonbalanced teams (Roberts &
Nason, 2004).
Back-Up Behavior
Findings from CSCW research.
Back-up behavior is
the ability to anticipate other team members’ needs through
accurate knowledge of their responsibilities and to shift the
workload among members to achieve balance during periods
of high workload or pressure, and is therefore related to
shared mental models and mutual performance monitoring
(Salas et al., 2005). Back-up can be provided through
feedback and coaching to improve performance, assisting
a teammate in performing a task, or completing a subtask
for a team member when work overload is detected (Marks,
Mathieu, & Zaccaro, 2001). In this sense, back-up behavior
directly influences team performance. Back-up behavior
is distinguished from “helping” in the sense that back-up
behavior is a response to the recognition of a genuine need
for assistance (Porter et al., 2010; Porter et al., 2003).
Implications for research on CSCL.
Research by
Molleman (2005) implies that back-up behavior may
Downloaded by [Jos Fransen] at 08:48 27 January 2013
18 FRANSEN, WEINBERGER, KIRSCHNER
sometimes be detrimental for learning-teams. His research
has shown that when someone takes over a subtask of a team
member for personal reasons not related to the team or task, it
can lead to “fault lines” within the team, mir roring the diver-
sity structure of a team, and potentially dividing a team into
subgroups, especially when conflicts arise and team commu-
nication decreases. Back-up behavior as a response to “free-
riding” or “social loafing” may equally impair collaborative
learning (Salomon & Globerson, 1989), as contributing to
the results is then left to the more motivated members in the
team.
Adaptability
Findings from CSCW research.
Adaptability is the
ability of a person or a group to adjust strategies through
back-up behavior and a reallocation of intrateam resources,
or by altering a course of action or team repertoire in response
to changing internal and external conditions based on infor-
mation gathered from the environment (Salas et al., 2005).
Adapting to new situations requires both the existence of
mutual performance monitoring and shared mental models,
especially an elaborate mental model of the final outcomes
(Chiocchio & Essiembre, 2009).
Implications for research on CSCL.
Although ex-
ternal conditions usually do not change or change only
marginally in the context of collaborative learning in educa-
tional settings, i nternal conditions may change which force
a learning-team to adapt its goals and/or strategies. To do so,
the team must be aware of changing internal conditions; all
team members must be infor med based on information that
is constantly being updated. With respect to CSCL environ-
ments, adaptability has been facilitated through the use of
awareness features, which inform learners about processes
and states of the team and its members (P. Kirschner, Strij-
bos, Kreijns, & Beers, 2004). Tools that facilitate awareness
often collect and aggregate information (e.g., on how partic-
ipation is distributed across a learning-team) and mirror that
information back to the learning-team (e.g., Kaplan et al.,
2009). Awareness tools may enable learners to analyze their
interactions and, thus, facilitate them to self-regulate and
adapt their behavior. However, to adequately decide on adapt-
ing goals and/or strategies the t eam must have developed
shared mental models with respect to team goals and distri-
bution of skills and expertise within the team (Zhou & Wang,
2010).
A CONCEPTUAL FRAMEWORK FOR TEAM
MATURATION IN CSCL
In the previous section we discussed the implications of
CSCW research for research on CSCL with respect to the
coordinating and suppor ting mechanisms conditional for up-
dating the five factors influencing team performance. In
this section we integrate our discussion of the implications
into a framework of team effectiveness in CSCL within the
perspective of learning-team development. The conceptual
framework presented here is the combination of the variables
elaborated upon and aspects of the TEAM model of group
development. The stages of this model provide a frame for
positioning the variables mediating team effectiveness, of-
fering insights into which variables are important in which
stage to attain team effectiveness. The model expands on ex-
isting models of learning-team effectiveness by integrating
variables mediating lear ning-team effectiveness derived from
a broad literature survey with a model of team development
that can be applied f or learning-teams in educational settings.
In the framework a distinction is made between coordinating
mechanisms and behavioral components (see Figure 2).
Coordinating mechanisms are conditional for updating
the behavioral components and for facilitating team devel-
opment. Behavioral components are process characteristics
of teams, some of which are directly related to team effec-
tiveness. Closed-loop communication is both a coordinating
mechanism and a behavioral component (i.e., actually com-
municating), because team members have to communicate
both effectively and transactively (i.e., b uild on one another’s
reasoning and exchange ideas, preferences, infor mation,
and feedback) to produce the outcomes of communication
conditional for effectively monitoring teamwork, deciding
on changing strategies and reallocating subtasks within the
team. Mutual performance monitoring (i.e., monitoring each
other’s performance), back-up behavior (i.e., anticipating
other team member’s needs), and adaptability (i.e., adjusting
the team and/or task strategies in response to environmental
demands) are behavioral components that describe the team’s
actions during task-work. Mutual performance monitoring
is crucial to the team’s understanding of a change in task
characteristics and/or of problems with the team’s workload
distribution and the extent to which mutual performance
monitoring, back-up behavior and/or adaptability mediate
performance. In a best-case scenario with a well-composed
team and unchanging task characteristics, mutual perfor-
mance monitoring should result in effective task execution
through participation of all team members in a manner that
is expected and that was agreed upon. If, based upon mutual
performance monitoring, workload distribution problems are
signaled, then back-up behavior will mediate performance
(i.e., workload distribution problems will be solved through
adequate back-up behavior). If, based upon mutual perfor-
mance monitoring, a change in task characteristics is sig-
naled as a result of environmental changes, then adaptability
will mediate performance (i.e., a change in goals and/or
strategies might require subtask reallocation and/or role
redivision within the team). If, based upon mutual per-
formance monitoring, a change in task characteristics
is signaled as well as a workload distribution problem
Downloaded by [Jos Fransen] at 08:48 27 January 2013
TEAM EFFECTIVENESS AND TEAM DEVELOPMENT IN CSCL 19
FIGURE 2 Framework with variables influencing or mediating learning-team effectiveness positioned within the perspective of learning-team
development.
due to the adaptation occurs, perfor mance is mediated
through adaptability and back-up behavior, respectively.
Team effectiveness, as dependent variable in the framework,
includes the quality of the team’s performance as well
as the perceived satisfaction of individual needs of team
members. Team orientation and team leadership are not
considered key variables in the context of learning-teams
in collaborative learning practices and therefore left out
of the framework, though both could be important in early
stages of learning-team development, because a shortage
of team orientation debilitates team performance (Hillyard
et al., 2010) and dominant leadership might result in an
early lock-in of participation diversity (Kapur et al., 2008).
Also, a specific type of centralized leadership aimed at
coordinating a team’s performance can emerge during the
transition phase if reforming leads to acknowledging the
necessity of adapting the team’s strategies and the need for
coordination of the process (Heckman et al., 2007).
The developmental perspective indicates the position
of these framework components and by indicating which
of these components are important in different stages of
teamwork, we are highlighting potential areas for future
investigation.
Mutual trust (i.e., the perception that team members per-
form as agreed and protect one another’s interests) is not
considered as crucial for the effectiveness of ad hoc learning-
teams but typically evolves during the process of teamwork,
starting with the build-up of calculus-based trust (i.e., tr ust
based on the expected competences of others) in early stages
until achieving knowledge-based trust (i.e., trust based on
the perceived expertise of others) in later stages. It is not
likely that identification-based trust (i.e., trust based on valu-
ing others regarding expertise, behavior, and as a person)
will emerge in short-term learning-teams, other than the
companion-based trust (i.e., trust based on emotional bonds
with others) that already existed as a result of friendships
between classmates. The impact of mutual trust on learning-
team effectiveness is considered limited, but minimal levels
of cognition-based trust (i.e., trust based on knowledge about
others) are necessary during both early and later stages of
teamwork for building shared mental models (Fransen et al.,
2011) and for smoothly passing through the transition phase
and to perform well (Greenberg, Greenberg, & Antonucci,
2007).
Building s hared mental models (i.e., a shared understand-
ing of task characteristics and the team’s expertise) in early
Downloaded by [Jos Fransen] at 08:48 27 January 2013
20 FRANSEN, WEINBERGER, KIRSCHNER
stages of teamwork to establish sufficient levels of team and
task awareness is conditional for achieving learning-team ef-
fectiveness (Fransen et al., 2011). This is especially the case
because learning-team members usually lack the expertise
needed to imagine an elaborate model of final outcomes and
depend on one another for successfully completing the task
because they have limited influence on environmental factors
mediating the team’s performance (Chiocchio & Essiembre,
2009). Accurate shared mental models influence learning-
team effectiveness directly as well the effectiveness of closed-
loop communication and mutual performance monitoring.
Mutual performance monitoring, back-up behavior, and
adaptability are important during the performing stages.
Closed-loop communication i s important throughout the
whole process of team collaboration, but particularly dur-
ing performing stages, to monitor the teamwork effectively.
Mutual performance monitoring is crucial during both per-
forming stages as well as the reforming stage to decide if
everything is on track and whether outcomes meet the in-
tended quality. Back-up behavior and adaptability are prob-
ably necessary only during the reforming stage and the sec-
ond performing stage when a learning-team has to speed
up team performance in order to complete the task and de-
liver results on time. Team effectiveness will be mediated
by back-up behavior when workload distribution problems
arise, mediated by adaptability in cases of changing environ-
mental demands, and mediated by both back-up behavior and
adaptability if tasks have to be reallocated within the team
as a result of changing goals and/or strategies to meet an
approaching deadline.
CONCLUSIONS
The purpose of this article was t o analyze the relevance of
the research on work-team effectiveness in organizational
settings for the research on learning-team effectiveness in
educational settings. Although the variables mediating the
effectiveness of teams in workplace settings are applicable
to learning-teams i n educational settings, some restrictions
are important. Learning-teams can differ fundamentally from
work-teams regarding the distribution of power and exper-
tise within the team (e.g., all team members have more or
less the same status and contribute more or less the same
limited expertise), influence on environment and resources
(e.g., learning assignments are fixed, resources are limited
and cannot be controlled, and learners cannot imagine elabo-
rate models of outcomes), the purpose of collaborating (e.g.,
learning is the most important goal, producing results is ad-
ditional), the necessity of efficiency (e.g., effective learning
may be mediated by costly debates and negotiations, and
suboptimal production), and the duration of teamwork (e.g.,
most learning-teams that have been investigated are short-
term teams). These characteristics do have an impact on the
exact nature of the variables mediating team effectiveness and
on the importance of their influence in the different phases of
teamwork. In addition, development of lear ning-teams may
be specific (e.g., focusing primarily on developing task skills
and less on team skills) as a result of a restricted duration of
teamwork, and of students acting pragmatically as a result
of balancing teamwork with competing personal interests, as
well as students perceiving deadlines to be met and grad-
ing as most important. Learning-team development can be
characterized as linear progressive to some extent, including
a transition phase when a deadline approaches. As a result
of that, the variables mediating l earning-team effectiveness
must be discussed within this framework of learning-team
development and in the perspective of learning-team charac-
teristics mentioned before.
Based upon the earlier discussion, what may be concluded
is as follows:
Shared mental models are conditional for learning-teams
to collaborate effectively, but at the same time they are
the objective of collaborative learning. Therefore, shared
mental models have to be considered a variable on two
levels.
Collaborating for restricted periods and student pragma-
tism and task-orientedness (e.g., getting it finished by
date X, and Y is enough for a passing grade) will prob-
ably impact the importance of mutual trust in learning-
teams.
In most situations there is no need for team leadership,
only coordination, although role division and inequality of
participation are important issues in collaborative learning
practices, which could be dealt with by assigning roles
and/or scripting collaboration.
Team orientation is vulnerable due to differing attitudes
of students toward collaborative learning as a result of
past experiences with teamwork. Although difficult to in-
fluence, it can be stimulated if students experience less
uncertainty through collaborating, if teams are kept small,
and if team composition is kept stable.
Back-up behavior is important, although the extent to
which learning-teams will show back-up behavior de-
pends on commitment to the team and to teamwork. Also,
backing-up may become helping out for reasons not re-
lated to team goals.
Adaptability is less important for learning-teams with
regard to responding at changing environments, as
assignments are fixed, and goals and deadlines are set
and usually will not change.
Communication, and more specifically closed-loop com-
munication, is important, although the nature of commu-
nication will depend on the type of learning task and task
complexity with communication for developing shared
mental models and monitoring the production process in
project-based learning and for debate and negotiation in
knowledge-construction situations.
The nature of mutual performance monitoring differs
according to the characteristics of an assignment with
Downloaded by [Jos Fransen] at 08:48 27 January 2013
TEAM EFFECTIVENESS AND TEAM DEVELOPMENT IN CSCL 21
mutual performance monitoring equaling the monitoring
of teamwork in workplace settings for project-based lear n-
ing but will be distributed in collaborative lear ning prac-
tices aiming at knowledge construction due to the trans-
active nature of learning.
The framework is an attempt to integrate theories of group
development into a context-specific model for lear ning-team
development; it discusses variables mediating learning-team
effectiveness within this perspective. It therefore contributes
to the need to address issues of why groups develop differ-
ently, how different aspects of interaction are linked together,
and what mechanisms underlie the transition from stability to
instability and back again (Arrow, Poole, Henry, Wheelan, &
Moreland, 2004). The framework also builds on the multiple
sequence model of group development (Poole, 1983), a dy-
namic contingency model of group development presenting
development as a process of continuously evolving tracks of
group activities that intertwine over time, more specifically
task process activities (i.e., managing the task), relational
activities (managing relationships among members), and a
topical focus (i.e., issues of concern to the group at given
points). If development on these tracks converges in a coher-
ent pattern, phases of group development may be recognized
(Poole, 1983), similar to the convergence of team and task
skills in the TEAM model (Morgan et al., 2001) which ex-
plains group development of ad hoc learning-teams probably
better. By combining the developmental perspective with the
adapted Big Five framework, presenting variables mediat-
ing learning-team effectiveness, relationships between the
task activities, the team activities and team development are
becoming more explicit by addressing how aspects of in-
teraction are linked together and what transitions a given
learning-team may face and why. Recent studies investi-
gating learning-team development and effectiveness by an-
alyzing team member participation based on the Big Five
framework did not offer information on the five components
for teams to decide on how to adequately manage team-
work because only visualizations were used presenting team
activity linked to aspects of the Big Five without norma-
tive information on what learning-teams are supposed to do,
assuming that if teams are aware of these interaction pat-
terns they may use this information to improve team per-
formance by taking normative decisions themselves (Kay,
Maisonneuve, Yacef, & Reimann, 2006). The framework we
present here offers insight into the relative importance of
variables mediating learning-team effectiveness in different
stages of learning-team development. The framework pro-
vides guidelines on what learning-teams are supposed to do
in order to become effective, although this has to be con-
firmed in future research. Research on team effectiveness in
collaborative learning settings should acknowledge the ex-
act nature of variables mediating learning-team effectiveness
and their influence in different stages of lear ning-team de-
velopment.
REFERENCES
Akkerman, S., Van den Bossche, P., Admiraal, W., Gijselaers, W., Segers,
M., Simons, R., & Kirschner, P. (2007). Reconsidering group cogni-
tion: From conceptual confusion to a boundary area between cognitive
and socio-cultural perspectives? Educational Research Review, 2, 39–63.
doi:10.1016/j.edurev.2007.02.001
Alfonseca, E., Carro, R., Mart
´
ın, E., Ortigosa, A., & Paredes, P. (2006). The
impact of learning styles on student grouping for collaborative learning:
A case study. User Modeling and User-Adapted Interaction, 16, 377–401.
doi:10.1007/s11257-006-9012-7
Alimo-Metcalfe, B., & Alban-Metcalfe, R. J. (2001). The develop-
ment of a new Transformational Leadership Questionnaire. Jour-
nal of Occupational and Organizational Psychology, 74, 1–27.
doi:10.1348/096317901167208
Arrow, H., Poole, M., Henry, K., Wheelan, S., & Moreland, R. (2004).
Time, change, and development: The temporal perspective on groups.
Small Group Research, 35, 73–105. doi:10.1177/1046496403259757
Bachmann, A. (2006). Melting pot or tossed salad? Implications for de-
signing effective multicultural workgroups. Management International
Review, 46, 721–748. doi:10.1007/s11575-006-0124-4
Bacon, D., Stewart, K., & Silver, W. (1999). Lessons from the best and
worst student team experiences: How a teacher can make the differ-
ence. Journal of Management Education, 23, 467–488. doi:10.1177/
105256299902300503
Barron, B. (2003). When smart groups fail. The Journal of Learning Sci-
ences, 12, 307–359. doi:10.1207/S15327809JLS1203
1
Beers, P., Boshuizen, H., Kirschner, P. A., & Gijselaers, W. (2006). Common
ground, complex problems and decision making. Group Decision and
Negotiation, 15, 529–556. doi:10.1007/s10726-006-9030-1
Bereiter, C. (2002). Education and mind in the knowledge age.Mahwah,
NJ: Erlbaum.
Blumenfeld, P., Soloway, E., Marx, R., Krajcik, J., Guzdial, M., &
Palincsar, A. (1991). Motivating project-based learning: Sustaining the
doing, supporting the learning. Educational Psychologist, 26, 369–398.
doi:10.1080/00461520.1991.9653139
Boud, D., Cohen, R., & Sampson, J. (1999). Peer learning and assess-
ment. Assessment & Evaluation in Higher Education, 24, 413–426.
doi:10.1080/0260293990240405
Bruffee, K. (1993). Collaborative learning: Higher education, interdepen-
dency, and the authority of knowledge. Baltimore, MD: Johns Hopkins
University Press. doi:10.2307/358879
Campion, M., Papper, E., & Medsker, G. (1996). Relations between work
team characteristics and effectiveness: A replication and extension. Per-
sonnel Psychology, 49, 429–452.
Cannon-Bowers, J., Salas, E., & Converse, S. (1993). Shared mental models
in expert team decision making. In N. Castellan (Ed.), Individual and
group decision making (pp. 221–246). Hillsdale, NJ: Erlbaum.
Chinn, C. A., O’Donnell, A. M., & Jinks, T. S. (2000). The structure of dis-
course in collaborative learning. The Journal of Experimental Education,
69, 77–97. doi:10.1080/00220970009600650
Chiocchio, F., & Essiembre, H. (2009). Cohesion and performance. Small
Group Research, 40, 382–420. doi:10.1111/j.1468-2370.2008.00239.x
Cho, H., Gay, G., Davidson, B., & Ingraffea, A. (2007). Social net-
works, communication styles, and learning performance in a CSCL
community. Computers & Education, 49, 309–329. doi:10.1016/
j.compedu.2005.07.003
Cohen, S., & Bailey, D. (1997). What makes teams work: Group effec-
tiveness research from the shop floor to the executive suite. Journal of
Management, 23, 239–290.
Cummings, J., & Cross, R. (2003). Structural properties of work groups
and their consequences for performance. Social Networks, 25, 197–210.
doi:10.1016/S0378-8733(02)00049-7
Dembo, M., & McAuliffe, T. (1987). Effects of perceived ability and grade
status on social interaction and influence in cooperative groups. Journal
of Educational Psychology, 79, 415–423.
Downloaded by [Jos Fransen] at 08:48 27 January 2013
22 FRANSEN, WEINBERGER, KIRSCHNER
Derry, S., DuRussel, L., & O’Donnell, A. (1998). Individual and distributed
cognitions in interdisciplinary teamwork: A developing case study and
emerging theory. Educational Psychology Review, 10, 25–56.
Dillenbourg, P. (1999). What do you mean by ‘collaborative learning’? In P.
Dillenbourg (Ed.), Collaborative learning: Cognitive and computational
approaches (pp. 1–19). Oxford, UK: Elsevier.
Dillenbourg, P., Baker, M., Blaye, A., & O’Malley, C. (1996). The evolution
of research on collaborative learning. In E. Spada & P. Reiman (Eds.),
Learning in humans and machine: Towards an interdisciplinary learning
science (pp. 189–211). Oxford, UK: Elsevier.
Driskell, J., Goodwin, G., Salas, E., & O’Shea, P. (2006). What makes a
good team player? Personality and team effectiveness. Group Dynamics,
10, 249–271. doi:10.1037/1089-2699.10.4.249
Durham, C., Knight, D., & Locke, E. (1997). Effects of leader role,
team-set goal difficulty, efficacy, and tactics on team effectiveness. Or-
ganizational Behavior and Human Decision Processes, 72, 203–231.
doi:10.1006/obhd.1997.2739
Eby, L., & Dobbins, G. (1997). Collectivistic orientation in teams: An
individual and group-level analysis. Journal of Organizational Behavior,
18, 275–295. doi:10.2307/3100145
Felder, R., & Brent, R. (2001). Effective strategies for cooperative learning.
Journal of Cooperation & Collaboration in College Teaching, 10, 69–75.
Feldman Barr, T., Dixon, A., & Gassenheimer, B. (2005). Exploring the ‘lone
wolf phenomenon in student teams. Journal of Marketing Education,
27(1), 81–90.
Ferrante, C., Green, S., & Forster, W. (2006). Getting more out of team
projects: Incentivizing leadership to enhance performance. Journal of
Management Education, 30, 788–797. doi:10.1177/1052562906287968
Fischer, F., Kollar, I., Mandl, H., & Haake, J. (Eds.). (2007). Scripting
computer-supported communication of knowledge. Cognitive, computa-
tional and educational perspectives. New York, NY: Springer.
Foels, R., Driskell, J., Mullen, B., & Salas, E. (2000). The effects of demo-
cratic leadership on g roup member satisfaction. Small Group Research,
31, 676–701. doi:10.1177/104649640003100603
Fransen, J., Kirschner, P. A., & Erkens, G. (2011). Mediating team ef-
fectiveness in the context of collaborative learning: The importance of
team and task awareness. Computers in Human Behavior, 27, 1103–1113.
doi:10.1016/j.chb.2010.05.017
Furst, S., Blackburn, R., & Rosen, B. (1999). Virtual team effectiveness:
A proposed research agenda. Information Systems Journal, 9, 249–
269.
Garrison, D., & Cleveland-Innes, M. (2005). Facilitating cognitive pres-
ence in online learning: Interaction is not enough. American Journal of
Distance Education, 19, 133–148. doi:10.1207/s15389286ajde1903
2
Gersick, C. (1988). Time and transition in work teams: Towards a new model
of group development. The Academy of Management Journal, 31, 9–41.
doi:10.2307/256496
Gillard, S., & Johansen, J. (2004). Project management communica-
tion: A systems approach. Journal of Information Science, 30, 23–29.
doi:10.1177/0165551504041675
Greenberg, P., Greenberg, R., & Antonucci, Y. (2007). Creating and
sustaining trust in virtual teams. Business Horizons, 50, 325–333.
doi:10.1016/j.bushor.2007.02.005
Hackman, J. (Ed.). (1990). Groups that work [and those that don’t]; Creating
conditions for effective teamwork. San Francisco, CA: Jossey-Bass.
Halfhill, T., Sundstrom, E., Lahner, J., Calderone, W., & Nielsen, T. (2005).
Group personality composition and group effectiveness: An integra-
tive review of empirical research. Small Group Research, 36, 83–105.
doi:10.1177/1046496404268538
Hannah, S., Uhl-Bien, M., Avolio, B., & Cavar retta, F. (2009). A framework
for examining leadership in extreme contexts. The Leadership Quarterly,
20, 897–919. doi:10.1016/j.leaqua.2009.09.006
Heckman, R., Crowston, K., & Misiolek, N. (2007). A structurational per-
spective on leadership in virtual teams. In K. Crowston, S. Sieber, &
E. Wynn (Eds.), Virtuality and virtualization (Vol. 236, pp. 151–168).
Boston, MA: Springer. doi:10.1007/978-0-387-73025-7
Hillyard, C., Gillespie, D., & Littig, P. (2010). University students’ attitudes
about learning in small groups after frequent participation. Active Learn-
ing in Higher Education, 11, 9–20. doi:10.1177/1469787409355867
Hinsz, V. (1995). Mental models of groups as social systems. Small Group
Research, 26, 200–233. doi:10.1177/1046496495262003
Hirokawa, R., Cathcart, R., Samovar, L., & Henman, L. (2003). Small
group communication: Theory and practice (8th ed.). Los Angeles, CA:
Roxbury.
Hogg, M., Abrams, D., Otten, S., & Hinkle, S. (2004). The so-
cial identity perspective. Small Group Research, 35, 246–276.
doi:10.4135/9781412990165
Hsu, J., Chang, J., Klein, G., & Jiang, J. (2011). Exploring the impact of
team mental models on information utilization and project performance
in system development. International Journal of Project Management,
29, 1–12. doi:10.1016/j.ijproman.2009.12.001
Janssen, J., Erkens, G., Kirschner, P., & Kanselaar, G. (2009). Influence of
group member familiarity on online collaborative learning. Computers in
Human Behavior, 25, 161–170.
Jarvenpaa, S., & Leidner, D. (1999). Communication and trust in global
virtual teams. Organzation Science, 10, 791–815.
Johnson, S., Suriya, C., Won Yoon, S., Berrett, J., & La Fleur, J. (2002). Team
development and group processes of virtual learning teams. Computers
& Education, 39, 379–393. doi:10.1016/S0360-1315(02)00074-X
Kanawattanachai, P., & Yoo, Y. (2002). Dynamic nature of trust in vir-
tual teams. Journal of Strategic Information Systems,
11, 187–213.
doi:10.1016/S0963-8687(02)00019-7
Kaplan, F., Do-Lenh, S., Bachour, K., Kao, G. Y., Gault, C., & Dillenbourg,
P. (2009). Interpersonal computers for higher education. In P. Dillenbourg,
J. Huang, & M. Cherubini (Eds.), Interactive artifacts and furniture sup-
porting collaborative work and learning (Vol. 10, pp. 129–145). New
York, NY: Springer.
Kapur, M., Voiklis, J., & Kinzer, C. (2008). Sensitivities to early exchange in
synchronous computer-supported collaborative learning (CSCL) groups.
Computers & Education, 51, 54–66.
Katz, N., Lazer, D., Arrow, H., & Contractor, N. (2004). Network
theory and small groups. Small Group Research, 35, 307–332.
doi:10.1177/1046496404264941
Kay, J., Maisonneuve, N., Yacef, K., & Reimann, P. (2006). The Big Five
and visualisations of team work activity. In M. Ikeda, K. Ashley, & T.
Chan (Eds.), Intelligent tutoring systems (Vol. 4053, pp. 197–206). Berlin,
Germany: Springer. doi:10.1007/11774303
20
Kayes, A., Kayes, D., & Kolb, D. (2005). Experiential learning in teams.
Simulation Gaming, 36, 330–354. doi:10.1177/1046878105279012
Kellermanns, F., Floyd, S., Pearson, A., & Spencer, B. (2008). The contingent
effect of constructive confrontation on the relationship between shared
mental models and decision quality. Journal of Organizational Behavior,
29, 119–137. doi:10.1002/job.497
Kerr, N., & Bruun, S. (1983). Dispensability of member effort and group
motivation losses: Free-rider effects. Journal of Personality and Social
Psychology, 44, 78–94.
Kiesler, S., & Cummings, J. (2002). What do we know about proxim-
ity and distance in work groups? A legacy of research. In P. Hinds &
S. Kiesler (Eds.), Distributed work (pp. 57–82). Cambridge, MA: MIT
Press.
Kiesler, S., Siegel, J., & McGuire, T. (1984). Social psychological as-
pects of computer-mediated communication. American Psychologist, 39,
1123–1134.
Kirschner, F., Paas, F., & Kirschner, P. (2009a). A cognitive load approach
to collaborative learning: United brains for complex tasks. Educational
Psychology Review, 21, 31–42. doi:10.1007/s10648-008-9095-2
Kirschner, F., Paas, F., & Kirschner, P. (2009b). Individual and group-
based learning from complex cognitive tasks: Effects on retention
and transfer efficiency. Computers in Human Behavior, 25, 306–314.
doi:10.1016/j.chb.2008.12.008
Kirschner, F., Paas, F., & Kirschner, P. (2011a). Superiority of collaborative
learning with complex tasks: A research note on an alternative affective
Downloaded by [Jos Fransen] at 08:48 27 January 2013
TEAM EFFECTIVENESS AND TEAM DEVELOPMENT IN CSCL 23
explanation. Computers in Human Behavior, 27, 53–57. doi:10.1016/
j.chb.2010.05.012
Kirschner, F., Paas, F., & Kirschner, P. (2011b). Task complexity as a driver
for collaborative learning efficiency: The collective working-memory ef-
fect. Applied Cognitive Psychology, 25, 615–624. doi:10.1002/acp.1730
Kirschner, P. (2002). Cognitive load theory: implications of cognitive load
theory on the design of learning. Learning and Instruction, 12, 1–10.
doi:10.1016/S0959-4752(01)00014-7
Kirschner, P., Beers, P., Boshuizen, H., & Gijselaers, W. (2008). Coercing
shared knowledge in collaborative learning environments. Computers in
Human Behavior, 24, 403–420. doi:10.1016/j.chb.2007.01.028
Kirschner, P., Strijbos, J., Kreijns, K., & Beers, P. (2004). Designing elec-
tronic collaborative learning environments. Educational Technology Re-
search & Development, 52(3), 47–66. doi:10.1007/BF02504675
Klimoski, R., & Mohammed, S. (1994). Team mental model: Construct or
metaphor? Journal of Management, 20, 403–437.
Krebs, S., Hobman, E., & Bordia, P. (2006). Virtual teams and
group member dissimilarity. Small Group Research, 37, 721–741.
doi:10.1177/1046496406294886
Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls
for social interaction in computer-supported collaborative learning envi-
ronments: A review of the research. Computers in Human Behaviour, 19,
335–353. doi:10.1016/S0747-5632(02)00057-2
Lea, M., Rogers, P., & Postmes, T. (2002). SIDE-VIEW: Evaluation of
a system to develop team players and improve productivity in Internet
collaborative learning groups. British Journal of Educational Technology,
33, 53. doi:10.1111/1467-8535.00238
Lea, M., Spears, R., & De Groot, D. (2001). Knowing me, know-
ing you: Anonymity effects on social identity processes within
groups. Personality and Social Psychology Bulletin, 27, 526–537.
doi:10.1177/0146167201275002
Leavitt, H. (1951). Some effects of certain communication patterns on group
performance. Journal of Abnormal and Social Psychology, 46, 38–50.
doi:10.1037/h0057189
Lewicki, R., & Bunker, B. (1996). Trust in relationships: A model of trust
development and decline. In R. Kramer & T. Tyler (Eds.), Trust in orga-
nizations (pp. 114–139). Thousand Oaks, CA: Sage.
Marks, M., Mathieu, J., & Zaccaro, S. (2001). A temporally based framework
and taxonomy of team processes. Academy of Management Review, 26,
356–367.
Mathieu, J., Heffner, T., Goodwin, G., Salas, E., & Cannon-Bowers, J.
(2000). The influence of shared mental models on team process and
performance. Journal of Applied Psychology, 85, 273–283.
McAllister, D. (1995). Affect- and cognition-based trust as foundations for
interpersonal cooperation in organizations. The Academy of Management
Journal, 38, 24–59. doi:10.2307/256727
Mehra, A., Smith, B., Dixon, A., & Robertson, B. (2006). Dis-
tributed leadership in teams: The network of leadership percep-
tions and team performance. The Leadership Quarterly, 17, 232–245.
doi:10.1016/j.leaqua.2006.02.003
Mennecke, B., Hoffer, J., & Wynee, B. (1992). The implications of group
development and history for group support system theory and practice.
Small Group Research, 23, 524–572. doi:10.1177/1046496492234005
Miller, B., Sun, J., Wu, X., & Anderson, R. (in press). Child leaders in
collaborative groups. Champaign: University of Illinois, Center for Study
of Reading.
Mohammed, S., & Dumville, B. (2001). Team mental models in a team
knowledge framework: Expanding theory and measurement across dis-
ciplinary boundaries. Journal of Organizational Behavior, 22, 89–106.
doi:10.1002/job.86
Molleman, E. (2005). Diversity in demographic characteristics, abilities and
personality traits: Do faultlines affect team functioning? Group Decision
and Negotiation, 14, 173–193. doi:10.1007/s10726-005-6490-7
Morgan, B., Salas, E., & Glickman, A. (2001). An analysis of team evolu-
tion and maturation. The Journal of General Psychology, 120, 277–291.
doi:10.1080/00221309.1993.9711148
Mu, S., & Gnyawali, D. R. (2003). Developing synergistic knowl-
edge in student groups. Journal of Higher Education, 74, 689.
doi:10.1353/jhe.2003.0040
Nembhard, I., & Edmondson, A. (2006). Making it safe: The effects of
leader inclusiveness and professional status on psychological safety and
improvement efforts in health care teams. Journal of Organizational Be-
havior, 27, 941–966. doi:10.1002/job.413
Newell, S., & Swan, J. (2000). Trust and inter-organizational networking.
Human Relations, 53, 1287–1328.
Peterson, R., & Behfar, K. (2003). The dynamic relationship between per-
formance feedback, trust, and conflict in groups: A longitudinal study.
Organizational Behavior and Human Decision Processes, 92, 102–112.
doi:10.1016/S0749-5978(03)00090-6
Poole, M. (1983). Decision development in small groups: A multiple se-
quence model of group decision development. Communication Mono-
graphs, 50, 321–341. doi:10.1080/03637758309390173
Porter, C., Gogus, C., & Chien-Feng Yu, R. (2010). When does
teamwork translate into improved team performance? A re-
source allocation perspective. Small Group Research, 41, 221–248.
doi:10.1177/1046496409356319
Porter, C., Hollenbeck, J., Ilgen, D., Ellis, A., West, B., & Moon, H.
(2003). Backing up behaviors in teams: The role of personality and
legitimacy of need. Journal of Applied Psychology, 88, 391–403.
doi:10.1037/0021–9010.88.3.391
Remedios, L., Clarke, D., & Hawthorne, L. (2008). The silent participant in
small group collaborative learning contexts. Active Learning in Higher
Education, 9, 201–216. doi:10.1177/1469787408095846
Rentsch, J., Heffner, T., & Duffy, L. (1994). What you know is what you
get from experience. Group & Organization Management, 19, 450–474.
doi:10.1177/1059601194194004
Roberts, A., & Nason, R. (2004, November–December). Quality not quan-
tity: Comparing interaction patterns of a balanced and non-balanced
team engaged in model-building within a computer-supported collabo-
rative learning (CSCL) environment. Paper presented at the International
Conference on Computers in Education, Melbour ne, Australia.
Roschelle, J. (1992). Learning by collaborating: Convergent con-
ceptual change. Journal of the Learning Sciences, 2, 235–276.
doi:10.1207/s15327809jls0203
1
Roschelle, J., & Teasly, S. (1995). The construction of shared knowl-
edge in collaborative problem solving. In C. O’Malley (Ed.), Knowl-
edge creation diffusion utilization (pp. 69–97). New York, NY: Springer.
doi:10.1007/978-3-642-85098-1
Salas, E., Sims, D., & Burke, C. (2005). Is there a ‘Big Five’ in teamwork?
Small Group Research, 36, 555–599. doi:10.1177/1046496405277134
Salomon, G., & Globerson, T. (1989). When teams do not function the way
they ought to. International Journal of Educational Research, 13, 89–99.
doi:10.1016/0883-0355(89)90018-9
Sangin, M., Molinari, G., N
¨
ussli, M., & Dillenbourg, P. (2011). Facilitating
peer knowledge modeling: Effects of a knowledge awareness tool on
collaborative learning outcomes and processes. Computers in Human
Behavior, 27, 1059–1067. doi:10.1016/j.chb.2010.05.032
Schellens, T., Van Keer, H., De Wever, B., & Valcke, M. (2007). Scripting by
assigning roles: Does it improve knowledge construction in asynchronous
discussion groups? International Journal of Computer-Supported Collab-
orative Learning, 2, 225–246. doi:10.1007/s11412-007-9016-2
Sessa, V., & London, M. (2007). Work group learning: Understanding,
improving & assessing how groups learn in organizations.Mahwah,NJ:
Erlbaum.
Shaw, M. (1954). Some effects of problem complexity upon problem solu-
tion efficiency in different communication nets. Journal of Experimental
Psychology, 48, 211–217. doi:10.1037/h0054191
Sheppard, B., & Sherman, D. (1998). The grammars of trust: A model and
general implications. The Academy of Management Review, 23, 422–437.
Smith, G. (2001). Group development: A review of the literature and a
commentary on future research directions. Group Facilitation: A Research
and Applications Journal, 3, 14–46.
Downloaded by [Jos Fransen] at 08:48 27 January 2013
24 FRANSEN, WEINBERGER, KIRSCHNER
Smith-Jentsch, K., Cannon-Bowers, J., Tannenbaum, S., & Salas, E. (2008).
Guided team self-correction: Impacts on team mental models, processes
and effectiveness. Small Group Research, 39, 303–327.
Stewart, G., & Barrick, M. (2000). Team structure and performance: As-
sessing the mediating role of intrateam process and the moderating
role of task type. The Academy of Management Journal, 43, 135–148.
doi:10.2307/1556372
Stout, R., Cannon-Bowers, J., Salas, E., & Milanovich, D. (1999).
Planning, shared mental models, and coordinated performance:
An empirical link is established. Human Factors: The Jour-
nal of the Human Factors and Ergonomics Society, 41, 61–71.
doi:10.1518/001872099779577273
Strijbos, J., & Fischer, F. (2007). Methodological challenges for col-
laborative learning research. Learning and Instruction, 17, 389–393.
doi:10.1016/j.learninstruc.2007.03.004
Strijbos, J., Martens, R., Jochems, W., & Broers, N. (2004). The ef-
fect of functional roles on group efficiency: Using multilevel mod-
eling and content analysis to investigate computer-supported col-
laboration in small groups. Small Group Research, 35, 195–229.
doi:10.1177/1046496403260843
Strijbos, J., Martens, R., Jochems, W., & Broers, N. (2007). The effect of
functional roles on perceived group efficiency during computer-supported
collaborative learning: A matter of triangulation. Computers in Human
Behavior, 23, 353–380. doi:10.1016/j.chb.2004.10.016
Teasley, S. (1997). Talking about reasoning: How important is the peer in
peer collaboration? In L. Resnick, R. S
¨
alj
¨
o, C. Pontecorvo, & B. Burge
(Eds.), Discourse, tools and reasoning: Essay on situated cognition (pp.
361–384). Berlin, Germany: Springer.
Tolmie, A., & Boyle, J. (2000). Factors influencing the success of com-
puter mediated communication (CMC) environments in university teach-
ing: A review and case study. Computers & Education, 34, 119–140.
doi:10.1016/S0360-1315(00)00008-7
Tuckman, B., & Jensen, M. (1977). Stages in small group devel-
opment revisited. Group and Organizational Studies, 2, 419–427.
doi:10.1177/105960117700200404
Underwood, J. (2003). Student attitudes towards socially acceptable and
unacceptable group working practices. British Journal of Psychology, 94,
319–337. doi:10.1348/000712603767876253
Van den Bossche, P. (2006). Minds in teams; The influence of social and
cognitive factors on team learning. Maastricht, Germany: Maastricht Uni-
versity.
Van den Bossche, P., Gijselaars, W., Segers, M., & Kirschner, P. A.
(2006). Social and cognitive factors driving teamwork in collabo-
rative learning environments. Small Group Research, 37, 490–521.
doi:10.1177/1046496406292938
Wang, S., & Lin, S. (2007). The effects of group composition of
self-efficacy and collective efficacy on computer-supported collab-
orative learning. Computers in Human Behavior, 23, 2256–2268.
doi:10.1016/j.chb.2006.03.005
Webb, N., & Palincsar, A. (1996). Group processes in the classroom. In D.
Berliner & M. Calfee (Eds.), Handbook of educational psychology (pp.
841–873). New York, NY: Simon & Schuster.
Wecker, C., & Fischer, F. (2011). From guided to self-regulated perfor-
mance of domain-general skills: The role of peer monitoring during the
fading of instructional scripts. Learning and Instruction
, 21, 746–756.
doi:10.1016/j.learninstruc.2011.05.001
Wegerif, R. (1998). The social dimension of asynchronous learning net-
works. The Journal of Asynchronous Learning Networks, 2, 34–49.
Weinberger, A. (2011). Principles of transactive computer-supported col-
laboration scripts. Nordic Journal of Digital Literacy, 6, 189–202.
Weinberger, A., Ertl, B., Fischer, F., & Mandl, H. (2005). Epistemic and
social scripts in computer–supported collaborative learning. Instructional
Science, 33, 1–30. doi:10.1007/s11251-004-2322-4
Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative
knowledge construction in computer-supported collaborative learning.
Computers & Education, 46, 71–95. doi:10.1016/j.compedu.2005.04.003
Weinberger, A., Stegmann, K., & Fischer, F. (2007). Knowledge conver-
gence in collaborative learning: Concepts and assessment. Learning and
Instruction, 17, 416–426. doi:10.1016/j.learninstruc.2007.03.007
Willcoxson, L. (2010). “It’s not fair”: Assessing the dynamics and re-
sourcing of teamwork. Journal of Management Education, 30, 798–808.
doi:10.1177/1052562906287964
Wilson, J., Strauss, S., & McEvily, B. (2006). All in due time: The de-
velopment of trust in computer-mediated and face-to-face teams. Or-
ganizational Behaviour and Human Decision Processes, 99, 16–33.
doi:10.1016/j.obhdp.2005.08.001
Wilson, K., Burke, C., Priest, H., & Salas, E. (2005). Promoting health care
safety through training high reliability teams. BJM Quality & Safety in
Health Care, 14, 303–309. doi:10.1136/qshc.2004.010090
Ying, Z., & Erping, W. (2010). Shared mental models as moderators of team
process-performance relationships. Social Behavior & Personality: An
International Journal, 38, 433–444.
Yoo, Y., & Kanawattanachai, P. (1993). Developments of transactive memory
systems and collective mind in virtual teams. International Journal of
Organizational Analysis, 9, 187–208. doi:10.1108/eb028933
Yoon, S. (2006). Two g roup development patterns of virtual learning teams.
Quarterly Review of Distance Education, 7, 297–312.
Zhou, Y., & Wang, E. (2010). Shared mental models as moderators of team
process–performance relationships. Social Behavior and Personality, 38,
433–444. doi:10.2224/sbp.2010.38.4.433
Downloaded by [Jos Fransen] at 08:48 27 January 2013
... Through their interaction, many opportunities emerge to present ideas, debate, defend or question arguments, evaluate and synthesize information coming up with a better understanding and a shared (and new) knowledge or solutions (Cottell & Millis, 1992), enabling the promotion of CCT (Warsah et al., 2021). This is explained by the fact that the confrontation of ideas often leads to a cognitive and socio-cognitive conflict and to an epistemic unfreezing (Fransen et al., 2013;Morais et al., 2017) triggering students' curiosity and search for more knowledge, which in turn results in the questioning of their own beliefs and knowledge. That is, when students are faced with different and equally well-grounded perspectives, uncertainty about the correctness of their initial position increases, which translates into the search for more information and a more adequate perspective to explain the situation under analysis. ...
... When engaged in CL groups, students increase and improve their individual level of participation to the discussions. Together with the epistemic unfreezing that occurs, their contributions become intellectually more relevant and valuable (Cottell & Millis, 1992;Fransen et al., 2013;Gillies, 2016;Morais et al., 2017). We didn't find ...
Article
Full-text available
This study investigated the effects of lecturing, cooperative learning and concept maps on the development of critical and creative thinking skills. A quasiexperimental non-randomized study involved a class of students from the 3rd year of Psychology and two classes from the 3rd year of Pre-service Elementary Teachers. The study ran for 15 weeks, one semester. In one of the Primary School Teaching classes, cooperative learning and concept maps were used and in the other only cooperative learning. The Critical and Creative Thinking test was applied to assess pre and post critical and creative thinking skills. The results show that students from the two classes that used cooperative learning and cooperative learning + concept maps improved further critical and creative thinking skills in relation to the lecturing class, with no significant differences between the first two. Pedagogical recommendations are made according to these results.
... teamwork suggests that while individual members' performance is related to team performance, other group-level factors, such as how team members collaborate with each other and utilise the resource and space available, also play essential roles (Fransen et al., 2013;Hall et al., 2018). For such assessments to be both reliable and developmental, teachers must be able to reliably evaluate the dynamics among multiple students and between students and procedural tasks within the simulation learning space (Lateef, 2010). ...
Article
Full-text available
Simulation‐based learning provides students with unique opportunities to develop key procedural and teamwork skills in close‐to‐authentic physical learning and training environments. Yet, assessing students' performance in such situations can be challenging and mentally exhausting for teachers. Multimodal learning analytics can support the assessment of simulation‐based learning by making salient aspects of students' activities visible for evaluation. Although descriptive analytics have been used to study students' motor behaviours in simulation‐based learning, their validity and utility for assessing performance remain unclear. This study aims at addressing this knowledge gap by investigating how indoor positioning analytics can be used to generate meaningful insights about students' tasks and collaboration performance in simulation‐based learning. We collected and analysed the positioning data of 304 healthcare students, organised in 76 teams, through correlation, predictive and epistemic network analyses. The primary findings were (1) large correlations between students' spatial‐procedural behaviours and their group performances; (2) predictive learning analytics that achieved an acceptable level (0.74 AUC) in distinguishing between low‐performing and high‐performing teams regarding collaboration performance; and (3) epistemic networks that can be used for assessing the behavioural differences across multiple teams. We also present the teachers' qualitative evaluation of the utility of these analytics and implications for supporting formative assessment in simulation‐based learning. Practitioner notes What is currently known about this topic Assessing students' performance in simulation‐based learning is often challenging and mentally exhausting. The combination of learning analytics and sensing technologies has the potential to uncover meaningful behavioural insights in physical learning spaces. Observational studies have suggested the potential value of analytics extracted from positioning data as indicators of highly‐effective behaviour in simulation‐based learning. What this paper adds Indoor positioning analytics for supporting teachers' formative assessment and timely feedback on students' group/team‐level performance in simulation‐based learning. Empirical evidence supported the potential use of epistemic networks for assessing the behavioural differences between low‐performing and high‐performing teams. Teachers' positively validated the utility of indoor positioning analytics in supporting reflective practices and formative assessment in simulation‐based learning. Implications for practitioners Indoor positioning tracking and spatial analysis can be used to investigate students' teamwork and task performance in simulation‐based learning. Predictive learning analytics should be developed based on features that have direct relevance to teachers' learning design. Epistemic networks analysis and comparison plots can be useful in identifying and assessing behavioural differences across multiple teams. What is currently known about this topic Assessing students' performance in simulation‐based learning is often challenging and mentally exhausting. The combination of learning analytics and sensing technologies has the potential to uncover meaningful behavioural insights in physical learning spaces. Observational studies have suggested the potential value of analytics extracted from positioning data as indicators of highly‐effective behaviour in simulation‐based learning. What this paper adds Indoor positioning analytics for supporting teachers' formative assessment and timely feedback on students' group/team‐level performance in simulation‐based learning. Empirical evidence supported the potential use of epistemic networks for assessing the behavioural differences between low‐performing and high‐performing teams. Teachers' positively validated the utility of indoor positioning analytics in supporting reflective practices and formative assessment in simulation‐based learning. Implications for practitioners Indoor positioning tracking and spatial analysis can be used to investigate students' teamwork and task performance in simulation‐based learning. Predictive learning analytics should be developed based on features that have direct relevance to teachers' learning design. Epistemic networks analysis and comparison plots can be useful in identifying and assessing behavioural differences across multiple teams.
... eLearning allows for collaborative activities in disciplines that rely on practical application as a demonstration of learning. Literature on the prominent pedagogical underpinnings of applied sciences and technology education denotes the use of social constructivism (Fransen, Weinberger & Kirschner, 2013), interactive lecturing and modelling and simulation (Saraswat, Anderson & Chircu, 2014). In addition, problem-based learning is viewed as a viable pedagogical approach in applied sciences and technology education practice. ...
Book
Full-text available
This book is an edited book that has been reviewed through a double-blind peer-review process. Each book chapter was reviewed by at least two different reviewers who are experts in their field. The chapters in the book have been edited and this publication has emerged as a result. The book consists of 7 chapters. Book chapter authors are reputable scientists from different countries of the world. The first chapter is a critical review and a case study in e-Business, with special attention to the digital currencies resource and its possibilities. It is an example of technovation for the improvement of personnel income and motivation, as a good practice of CSR 3.0. The study explains how it works this win-win practice, with a real example of a Spanish company. The second chapter attempts to incorporate the Unified Theory of Acceptance and Use of Technology (UTAUT) model with perceived risk theory (security risk and privacy risk) to explore its impact towards the intention to use m-government services. Age, gender and education level were also adopted as moderator variables to provide an in-depth understanding of citizens’ preference in m-government services. Partial Least Square (PLS) Structural Equation Modelling method was conducted. The third chapter aims to assess the level of gender inclusivity in the municipal e-procurement processes in the City of Johannesburg as a case study. It uses a Gender and Development (GAD) approach. Among the questions raised in the chapter are whether gender mainstreaming is considered in the municipal procurement processes; and if there are any initiatives in place to capacitate men and women to ensure their participation in the e-procurement processes. The fourth chapter examines the impediments that derail the intensive uptake of eLearning programmes in a particular higher education institution. The study adopted an inductive research paradigm that followed a qualitative research strategy. Data were collected by means of one-on-one in-depth interviews from selected faculty members at a nominated institution of higher learning. The fifth chapter investigated the role of Knowledge Management Systems (KMS) in enhancing the export performance of firms operating within the manufacturing sector in Zimbabwe. The study used a quantitative approach in which a survey questionnaire was distributed to 555 managers drawn from 185 manufacturing firms based in Harare. Data analyses involved the use of descriptive statistics, Spearman correlations and regression analysis. In the sixth chapter, a survey was undertaken on 131 small and medium-sized enterprises (SMEs) from Pelagonija region in order to determine the current level of SME digitalization within the region. It is aimed to compare with European Union (EU) average and to make conclusions on the impact of the SME digitalization to region gross domestic product (GDP) growth as well as revenues collection. The last chapter's purpose was to develop a measuring and modelling framework/instrument of Internet banking service quality (IBSQ) for the South African banking sector. Snowball and convenience sampling, both non-probability techniques were used to recruit participants for the study. A total of 310 Internet banking customer responses were utilised in the analysis.
... Working in teams is not only for successful completion of assignments. This is because by ensuring shared learning goals, learners can be challenged socially and emotionally, as they need to listen to different perspectives and are required to articulate and defend their ideas (Fransen et al., 2013;Pegrum et al., 2015). By doing so, the learner constructs new knowledge, makes meaning of the knowledge, and practices critical thinking, all skills which can be transferred to the workplace (DeWitt et al., 2014;Liu et al., 2016). ...
Chapter
For many postgraduate research students, months—sometimes years—may pass before their research projects and thesis writing are assessed. Assessment of a student’s research thesis usually occurs in the form of milestone presentations in which the student must defend their proposal, their findings and finally their entire thesis before a panel of assessors. However, in addition to these summative-type assessments (i.e. seminar presentations, thesis examination, viva voce) in reality, the thesis supervisory journey actually includes (and is often dominated by) a series of informal formative assessmentFormative assessment-for-learningLearning (AFL) events. This chapter presents one mobile-supervisor’s reflections on the use of an instant messaging (IM) platform, WhatsApp, as a tool for informal assessment-for-learning for individual student research and thesis writing, taking advantage of WhatsApp’s immediacy, multimodality and archival affordances allowing for “just-in-time” and “byte/bite-sized” assessment and feedbackFeedback.
... Working in teams is not only for successful completion of assignments. This is because by ensuring shared learning goals, learners can be challenged socially and emotionally, as they need to listen to different perspectives and are required to articulate and defend their ideas (Fransen et al., 2013;Pegrum et al., 2015). By doing so, the learner constructs new knowledge, makes meaning of the knowledge, and practices critical thinking, all skills which can be transferred to the workplace (DeWitt et al., 2014;Liu et al., 2016). ...
Chapter
For many years, assessment for laboratory work was done based on students’ written laboratory reports and not their practicalPractical competence, resulting in them struggling to perform their final year projects. Therefore, in our biochemistry course, we conducted a laboratory practical test consisting of four questions: one question addresses psychomotor domain Level 2 (Guided Response), whilst the other three questions address Level 3 (Mechanism). In the one-hour test, students were given 15 minutes to complete each task. Performance-based assessment was particularly done on solution dilution to see students’ execution of tasks, whilst assessment on the other three questions was based on results produced by the students. We found that students became more involved in the learningLearning process upon knowing that their practical competencyCompetency will be assessed during the semester. Students could also identify their weaknesses and took effort to improve themselves prior to their final year projects. Issues and challenges in implementing laboratory tests include large number of students, availability of resources, the absence of trained laboratory assistants, lack of materials and longer time duration. In addition, most instructors thought it was challenging as a performance-based approach requires too much time to be designed, administered and scored. Despite the challenges, we strongly believe that any science-based degree programme should apply skill-based assessment. Courses with laboratory practical classes should move from assessing students based on their laboratory reports to practical laboratory tests, as this would reflect their real technical skills competencyCompetency.
Article
Online collaboration is becoming increasingly more common in work life and education, a development that is accentuated by the Covid-19 pandemic. It is thus imperative that students learn to work in and as teams in online settings, and that teachers and educational researchers and policymakers understand how online environments enable and constrain student collaboration. However, what has been missing in research on online student collaboration is a focus on students as agents rather than passive learners as well as a lack of focus on student teams as self-organizing teams. This paper reports on a study that investigated the experiences of 1611 graduate students in 315 teams enrolled in an interdisciplinary project-based course during their (forced) transition from face-to-face to online collaboration due to the COVID-19 pandemic. We explored how the transition to online learning affected social interaction and how teams changed their practices to support and sustain social interaction in the online environment. The findings show that the changed conditions of the learning environment influenced social interaction in negative ways, but also that team reflection seemed to enable the students to reverse some of the adverse effects and develop practices that supported both the cognitive and socio-emotional dimensions of social interaction. Theoretically, this study suggests possible causes for why social interaction was reduced and provides in-depth knowledge about the relationships between social interaction, social presence, and social space. The study also provides support for theories of learning that emphasize the need to consider students as active agents rather than merely users of the affordances of a virtual learning environment or guided by the teacher's interventions. It makes a unique contribution to the scarce empirical literature on virtual self-organizing student teams in higher education and provides practical implications for teachers and educational researchers and policy makers.
Article
Computer-supported collaborative learning (CSCL) environments may at times become socio-emotionally tense and pose challenges that may have detrimental effects on mutual trust and shared mental models. Objective. This study aims to examine and classify general teamwork challenges in a novel but authentic CSCL setting (hackathon) to identify challenges that impede the development of key team coordination mechanisms (i.e., mutual trust and shared mental models). Methods. Multimodal data including responses to an adapted questionnaire (AIRE), post-competition interview data, and videos of team interaction were collected during an international hackathon event (N = 48, 71% male, M = 22 years age). Qualitative theory-driven coding and theme development were used to analyze the multimodal dataset (Greeno, 2006; Jarvenoja et al., 2013). Results. Our analyses revealed 16 general challenges that hamper teamwork in a hackathon. A model of team challenges was developed to categorize challenges into macro level themes including cognitive, motivational, emotional and behavioral challenges. We also identified which challenges hindered the development of mutual trust, and which challenges hindered the development of shared mental models. Conclusions. These findings provide important insights for educators and mentors in understanding the types of teamwork challenges that may occur in CSCL settings. The results also inform educators which challenges likely lead to mutual trust breakdown and weaken shared mental model bonds.
Article
Considering the crucial role of cross-cultural virtual learning teams (VLTs) in industries and academics, this study adopts a longitudinal approach and investigates in-depth how cross-cultural VLTs collaborate effectively by examining relationships among three concepts, namely swift trust, team trust, and shared mental model (SMM). Categorizing team stages as structuring, work, and termination, our study indicates that swift trust enhances team trust at the structuring stage. At all three stages, team trust strengthens SMM, which then improves team performance. At the work stage, the impact of SMM on team performance reaches its peak. Our findings contribute to the online learning literature and practices.
Book
Research on Computer-Supported Collaborative Learning (CSCL) is a multidisciplinary field located at the intersection of cognitive psychology, computer science, and education. Yet, the different epistemological and theoretical backgrounds of these disciplines often make fruitful exchange between them difficult. CSCL urgently needs to develop and use boundary concepts that can bring these fields closer together to improve cumulative research and development of computer-supported learning environments. Scripting Computer-Supported Collaborative Learning focuses on one term with the potential to become a real boundary concept in CSCL—"scripting". Scripting Computer-Supported Collaborative Learning, which has collected advanced script approaches, demonstrates the opportunities for using synergy to apply the script concept between perspectives and interdisciplinary CSCL approaches to scripting. This volume represents the state of the art of research on scripting computer-supported collaborative learning and provides a starting point for the development of a common understanding of scripting in CSCL. Research on collaboration scripts has an extraordinary potential for advancing the multidisciplinary endeavor of CSCL research and this book provides a rich basis for further exploring and realizing this potential. As such, it will be a valuable resource for research, development, and teaching.
Book
Although research in collaborative learning has a fairly long history, dating back at least to the early work of Piaget and Vygotsky, it is only recently that workers have begun to apply some of its findings to the design of computer based learning systems. The early generation of the!le systems focused on their potential for supporting individual learning: learning could be self­ paced; teaching could be adapted to individual learners' needs. This was certainly the promise of the later generation of intelligent tutoring systems. However, this promise has yet to be realised. Not only are there still some very difficult research problems to solve in providing adaptive learning systems, but there are also some very real practical constraints on the widespread take up of individualised computer based instruction. Reseachers soon began to realise that the organisational, cultural and social contexts of the classroom have to be taken into account in designing systems to promote effective learning. Much of the work that goes on in classrooms is collaborative, whether by design or not. Teachers also need to be able to adapt the technology to their varying needs. Developments in technology, such as networking, have also contributed to changes in the way in which computers may be envisaged to support learning. In September 1989, a group of researchers met in Maratea, Italy, for a NATO-sponsored workshop on "Computer supported collaborative . learning". A total of 20 researchers from Europe (Belgium.
Article
Inter-organizational and multi-functional networking are increasingly portrayed as new and potentially more effective forms of organization, especially where innovation is important. This is as true for academic work undertaken within universities as it is for business organizations; multi-disciplinary and inter-institutional collaboration being specified as highly desirable by the major research funding bodies. Integrating mechanisms are essential if such networks are to be effective in co-ordinating the work of a diverse range of partners. Importantly, they are required for the development of trust. Thus, the literature stresses that trust between the parties is central to the effective operation of such networks. This paper explores the evolution of trust within a particular inter-university, multi-disciplinary research team, and develops a model depicting the development and interrelation of different types of trust within this network. The difficulties this research network experienced in developing trust raise a more general question about the effectiveness of interdisciplinary research.
Article
Project-based learning is a comprehensive approach to classroom teaching and learning that is designed to engage students in investigation of authentic problems. In this article, we present an argument for why projects have the potential to help people learn; indicate factors in project design that affect motivation and thought; examine difficulties that students and teachers may encounter with projects; and describe how technology can support students and teachers as they work on projects, so that motivation and thought are sustained.
Article
The fading of instructional scripts can be regarded as necessary for allowing learners to take over control of their cognitive activities during the acquisition of skills such as argumentation. There is, however, the danger that learners might relapse into novice strategies after script prompts are faded. One possible solution could be monitoring by a peer with respect to the performance of the strategy to be learned. We conducted a 2×2-factorial experiment with 126 participants with fading and peer monitoring as between-subjects factors to test the assumptions that (1) the combination of a faded script and peer monitoring has a positive effect on strategy knowledge compared to only one or none of the two types of support; and (2) this effect is due to a greater amount of self-regulated performance of the strategy after the fading of the script when peer monitoring takes place. The findings support these assumptions. (http://www.sciencedirect.com/science/article/pii/S0959475211000387)
Chapter
Using data from two studies of scientific reasoning, this chapter explores whether transactive discussion is the basis of productive peer collaborations and questions what role the partner plays in the apparent effectiveness of this type of discussion. In the first study, dyads who engaged in transactive discussion showed more improvement than dyads who did not have transactive discussions. In the second study, both dyads and children working alone showed improvement related to talk in general. However, dyads produced more transactive types of talk and showed a more complex understanding of the problem that they generated more quickly. Having a partner was not a necessary or sufficient condition for producing transactive talk but increased likelihood that it would occur. The data from these studies suggest that the value of peer collaborations may be that the presence of a partner provides a natural context for elaborating one’s own reasoning.
Article
In this study we investigated how a natural status characteristic (grade level) and an experimentally induced status characteristic (ability) combine to affect group interaction and interpersonal perception in homogeneous and heterogeneous groups. Eighty male fifth and sixth graders were randomly assigned to groups of four that were made into homogeneous or heterogeneous "ability" groups on the basis of a bogus aptitude test. Their social interaction was videotaped as groups worked on a group consensus task. The data indicated that the two status characteristics (actual and induced) had a similar and significant effect on the social interaction in the groups. High-status students dominated group interaction, were more influential, and were more likely to be perceived as leaders. The data also indicated that differences in helping behavior may be due, in part, to the perceived status of the student and not only to the student's ability to give help.