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: Elsevier.
The evolution of research
on collaborative learning
P. Dillenbourg (Université de Genève, Switzerland)
M. Baker (CNRS, France)
A. Blaye (Université de Provence à Aix, France)
C. O'Malley (University of Nottingham, UK)
Abstract. For many years, theories of collaborative learning
tended to focus on how individuals function in a group. More
recently, the focus has shifted so that the group itself has become
the unit of analysis. In terms of empirical research, the initial goal
was to establish whether and under what circumstances
collaborative learning was more effective than learning alone.
Researchers controlled several independent variables (size of the
group, composition of the group, nature of the task,
communication media, and so on). However, these variables
interacted with one another in a way that made it almost
impossible to establish causal links between the conditions and the
effects of collaboration. Hence, empirical studies have more
recently started to focus less on establishing parameters for
effective collaboration and more on trying to understand the role
which such variables play in mediating interaction. In this chapter,
we argue that this shift to a more process-oriented account
requires new tools for analysing and modelling interactions.
For many years, theories of collaborative learning tended to focus on how
individuals function in a group. This reflected a position which was dominant
both in cognitive psychology and in artificial intelligence in the 1970s and
early 1980s, where cognition was seen as a product of individual information
processors, and where the context of social interaction was seen more as a
background for individual activity than as a focus of research in itself. More
recently, the group itself has become the unit of analysis and the focus has
shifted to more emergent, socially constructed, properties of the interaction.
In terms of empirical research, the initial goal was to establish whether and
under what circumstances collaborative learning was more effective than
learning alone. Researchers controlled several independent variables (size of
the group, composition of the group, nature of the task, communication media,
and so on). However, these variables interacted with one another in a way that
made it almost impossible to establish causal links between the conditions and
the effects of collaboration. Hence, empirical studies have more recently
started to focus less on establishing parameters for effective collaboration and
more on trying to understand the role which such variables play in mediating
interaction. This shift to a more process-oriented account requires new tools
for analysing and modelling interactions.
This chapter presents some of the major developments over recent years in
this field, in both theoretical and empirical terms, and then considers the
implications of such changes for tools and methods with which to observe and
analyse interactions between learners. In so doing, we have tried to address
both the work done in psychology and in distributed artificial intelligence
(DAI). However, we have to acknowledge that this chapter has a bias towards
psychology — not only because it reflects the interests of the authors to a large
extent, but also because DAI has focused more on cooperative problem
solving than on collaborative learning.
At this point we need to make a brief comment on this distinction: learning
versus problem solving and collaboration versus cooperation. While
psychologists consider that learning and problem solving are similar
processes, computer scientists still address them separately. Different research
communities (DAI versus machine learning, for example) have developed
different techniques, some for learning and some for problem solving. The
'collaboration' versus 'cooperation' debate is more complex. Some people use
these terms interchangeably. (Indeed, there is some disagreement amongst the
authors themselves.) For the purposes of this chapter, in acknowledgement of
distinctions that others in the field have made, we stick to a restricted
definition of the terms. “Collaboration" is distinguished from "cooperation" in
that cooperative work "... is accomplished by the division of labor among
participants, as an activity where each person is responsible for a portion of
the problem solving...", whereas collaboration involves the "... mutual
engagement of participants in a coordinated effort to solve the problem
together." (Roschelle & Teasley, in press).
Defining collaboration by the non-distribution of labour does not avoid
ambiguities. Miyake has shown that some spontaneous division of labour may
occur in collaboration: "The person who has more to say about the current
topic takes the task-doer's role, while the other becomes an observer,
monitoring the situation. The observer can contribute by criticising and giving
topic-divergent motions, which are not the primary roles of the task-doer."
(Miyake, 1986; p. 174). O'Malley (1987) reported similar results with pairs
attempting to understand the UNIX C-shell command interpreter. This
distribution of roles depends on the nature of the task and may change
frequently. For example, in computer-supported tasks, the participant who
controls the mouse tends to be "executor", while the other is likely to be the
"reflector" (Blaye, Light, Joiner, & Sheldon, 1991). Cooperation and
collaboration do not differ in terms of whether or not the task is distributed,
but by virtue of the way in which it is divided: in cooperation, the task is split
(hierarchically) into independent subtasks; in collaboration, cognitive
processes may be (heterarchically) divided into intertwined layers. In
cooperation, coordination in only required when assembling partial results,
while collaboration is "... a coordinated, synchronous activity that is the result
of a continued attempt to construct and maintain a shared conception of a
problem" (Roschelle & Teasley, in press).
2. Theoretical Issues: the individual or the group as the unit
What is the nature of the dyad in collaborative learning? It can be viewed as
comprising two relatively independent cognitive systems which exchange
messages. It can also be viewed as a single cognitive system with is own
properties. These two different answers to the question serve to anchor the two
ends of the theoretical axis. At one end, the unit of analysis is the individual.
The goal for research is to understand how one cognitive system is
transformed by messages received from another. At the other end of the axis,
the unit of analysis is the group. The challenge is to understand how these
cognitive systems merge to produce a shared understanding of the problem.
Along this axis, between the ‘individual' and the 'group', we can find three
different theoretical positions: socio-constructivist, socio-cultural and shared
(or distributed) cognition approaches.
In this chapter we talk about an ‘evolution’ along this axis because the social
end has recently received more attention — maybe because it has been
previously neglected. We do not mean to imply than one viewpoint is better
than another: scientists need both pictures from microscopes and pictures from
satellites. Moreover, for the sake of exposition, the approaches will be
presented as more different than they actually are. Both Piaget and Vygotsky
acknowledge the intertwined social and individual aspects of development
2.1. The socio-constructivist approach
Although Piaget's theory focused mainly on individual aspects in cognitive
development, it inspired a group of psychologists (the so-called “Genevan
School”) who in the 1970s undertook a systematic empirical investigation of
how social interaction affects individual cognitive development (cf. Doise &
Mugny, 1984). These researchers borrowed from the Piagetian perspective its
structural framework and the major concepts which were used to account for
development: conflict and the coordination of points of view (centrations).
This new approach described itself as a socio-constructivist approach: it
enhanced the role of inter-actions with others rather than actions themselves.
The main thesis of this approach is that "...it is above all through interacting
with others, coordinating his/her approaches to reality with those of others,
that the individual masters new approaches" (Doise, 1990, p.46). Individual
cognitive development is seen as the result of a spiral of causality: a given
level of individual development allows participation in certain social
interactions which produce new individual states which, in turn, make
possible more sophisticated social interaction, and so on.
Despite this theoretical claim, which suggests a complex intertwining between
the social and the individual plane, the experimental paradigm used by its
proponents involved two supposedly "individual" phases (pre- and post-test),
separated by an intervention session in which subjects worked either alone
(control condition) or in pairs. Evidence showed that, under certain conditions,
peer interaction produced superior performances on individual post-test than
individual training (for reviews, see Doise & Mugny, 1984; Blaye, 1988). The
studies which established this tradition of research involved children in the
age-range 5-7 years, and relied essentially on Piagetian conservation tasks.
Where working in pairs facilitated subsequent individual performance, the
mediating process was characterised as "socio-cognitive conflict", i.e. conflict
between different answers based on different centrations, embodied socially in
the differing perspectives of the two subjects. The social dimension of the
situation was seen as providing the impetus towards or catalyst for resolving
the conflict. Such resolution could be achieved by transcending the different
centrations to arrive at a more advanced "decentred" solution.
From this perspective, the question was asked: under which conditions might
socio-cognitive conflict be induced? One answer was to pair children who
were, from a Piagetian perspective, at different stages of cognitive
development. However, it was emphasised that subsequent individual progress
cannot be explained by one child simply modelling the other, more advanced,
child. It has been repeatedly demonstrated that "two wrongs can make a right"
(Glachan & Light, 1981). What is at stake here, then, is not imitation but a co-
ordination of answers. Subjects at the same level of cognitive development but
who enter the situation with different perspectives (due to spatial organisation,
for instance) can also benefit from conflictual interactions (Mugny, Levy &
Doise, 1978; Glachan & Light, 1982).
Researchers in DAI report similar empirical results. Durfee et al (1989)
showed that the performance of a network of problem solving agents is better
when there is some inconsistency among the knowledge of each agent. Gasser
(1991) pointed out the role of multiple representations and the need for
mechanisms for reasoning among multiple representations (see Saitta, this
volume). These findings concern the heterogeneity of a multi-agent system.
Bird (1993) discriminates various forms of heterogeneity: when agents have
different knowledge, use various knowledge representation schemes or use
different reasoning mechanisms (induction, deduction, analogy, etc.). For
Bird, heterogeneity is one of the three dimensions that define the design space
for multi-agent systems. The other dimensions, distribution and autonomy,
will be discussed later.
The success of the concept of conflict in computer systems is not surprising.
This logical concept can be modelled in terms of knowledge or beliefs and
integrated in truth maintenance systems or dialogue models. However, the
main proponents of socio-cultural theory now admit that their view has
probably been too mechanistic (Perret-Clermont et al., 1991). Blaye's
empirical studies (Blaye, 1988) have highlighted the limits of "socio-cognitive
conflict" as "the" underlying causal mechanism of social facilitation of
cognitive development. Disagreement in itself seems to be less important than
the fact that it generates communication between peer members (Blaye, 1988;
Gilly, 1989). Bearison et al. (1986) reported that non-verbal disagreement
(manifested for instance by moving the object positioned by the partner) was
not predictive of post-test gains.
The role of verbalisation may be to make explicit mutual regulation processes
and thereby contribute to the internalisation of these regulation mechanisms by
each partner (Blaye, 1988). This interpretation leads us to the socio-cultural
theory discussed in the next section.
2.2. The socio-cultural approach
The second major theoretical influence comes from Vygotsky (1962, 1978)
and researchers from the socio-cultural perspective (Wertsch, 1979, 1985,
1991; Rogoff, 1990). While the socio-cognitive approach focused on
individual development in the context of social interaction, the socio-cultural
approach focuses on the causal relationship between social interaction and
individual cognitive change. The basic unit of analysis is social activity, from
which individual mental functioning develops. Whereas a Piagetian approach
sees social interaction as providing a catalyst for individual change, often
dependent upon individual development, from a Vygotskian perspective, inter-
psychological processes are themselves internalised by the individuals
involved. Vygotsky argued that development appears on two planes: first on
the inter-psychological, then on the intra-psychological. This is his “genetic
law of cultural development”. Internalisation refers to the genetic link between
the social and the inner planes. Social speech is used for interacting with
others, inner speech is used to talk to ourselves, to reflect, to think. Inner
speech serves the function of self-regulation.
A simple computational model of internalisation has been developed by
Dillenbourg and Self (1992). The system includes two agents able to argue
with each other. The agent's reasoning is implemented as an argumentation
with itself (inner speech). Each learner stores the conversations conducted
during collaborative problem solving and re-instantiates elements from the
dialogue for its own reasoning. The learner may for instance discard an
argument that has been previously refuted by its partner in a similar context.
The psychological reality is of course more complex, what takes place at the
inter-psychological level is not merely copied to the intra-psychological, but
involves an active transformation by the individual.
The mechanism through which participation in joint problem solving may
change the understanding of a problem is referred to as “appropriation”
(Rogoff, 1991). Appropriation is the socially-oriented version of Piaget's
biologically-originated concept of assimilation (Newman, Griffin and Cole,
1989). It is a mutual process: each partner gives meaning to the other's actions
according to his or her own conceptual framework. Let us consider two
persons, A and B, who solve a problem jointly. A performs the first action. B
does the next one. B's action indicates to A how B interpreted A's first action.
Fox (1987) reported that humans modify the meaning of their action
retrospectively, according to the actions of others that follow it. From a
computational viewpoint, this mechanism of appropriation requires a high
level of opportunism from agent-B, which must integrate agent-A's
contribution, even if this action was not part of his plans.
Like the previous approach, this theory also attaches significance to the degree
of difference among co-learners. Vygotsky (1978) defined the “zone of
proximal development” as “...the distance between the actual developmental
level as determined by independent problem solving and the level of potential
development as determined through problem solving under adult guidance or
in collaboration with more capable peers.” We will see that this concept is
important to understand some empirical results.
Research in DAI does not directly refer to Vygotskian positions. This is
somewhat surprising since the issue of regulation, which is central to the
socio-cultural theory, is also a major issue in DAI. In computational terms,
regulation is more often referred to as a an issue of 'control' or 'autonomy'. For
Bird (1993), it constitutes the second dimension of the design space for multi-
agent systems. As in political structures, there exist centralised systems where
control is achieved by a super-agent or a central data structure (e.g.,
blackboard architectures) and decentralised systems in which each agent has
more autonomy. An agent is more autonomous if it executes local functions
without interference with external operations (execution autonomy), if it
chooses when and with whom it communicates (communication autonomy)
and whether it self-organises into hierarchical, serial or parallel sub-processes
(structural autonomy) (Bird, 1993).
2.3. The shared cognition approach
The concept of shared cognition is deeply intertwined with the 'situated
cognition' theory (Suchman, 1987; Lave, 1988 — see also Mandl, this
volume). For those researchers, the environment is an integral part of
cognitive activity, and not merely a set of circumstances in which context-
independent cognitive processes are performed. The environment includes a
physical context and a social context. Under the influence of sociologists and
anthropologists, the focus is placed largely on the social context, i.e. not only
the temporary group of collaborators, but the social communities in which
these collaborators participate.
This approach offers a new perspective on the socio-cognitive and the socio-
cultural approaches, and has recently led to certain revisions by erstwhile
proponents of the earlier theories. Perret-Clermont et al. (1991), for example,
question the experimental settings they had previously used for developing the
socio-constructivist approach. They noticed that their subjects tried to
converge toward the experimenter's expectations. The subjects' answers were
influenced by the meaning they had inferred from their social relationship with
the experimenter. Wertsch (1991) makes similar criticisms against work in the
socio-cultural tradition: social interactions are studied as if they occur outside
a social structure. Through language, we acquire a culture which is specific to
a community. For instance, we switch grammar and vocabulary rapidly
between an academic seminar room and the changing rooms of a sports centre.
But overall, beyond a vocabulary and a grammar, we acquire a structure of
social meanings and relationships (Resnick, 1991) that are fundamental for
future social interactions.
This approach challenges the methodology used in many experiments where
the subjects perform post-tests individually, often in a laboratory setting. More
fundamentally, this approach questions the theoretical bases on which the
previous ones rely: "... research paradigms built on supposedly clear
distinctions between what is social and what is cognitive will have an inherent
weakness, because the causality of social and cognitive processes is, at the
very least, circular and is perhaps even more complex" (Perret-Clermont,
Perret and Bell, 1991, p. 50). Collaboration is viewed as the process of
building and maintaining a shared conception of a problem (Roschelle &
Teasley, in press). While the previous approaches were concerned with the
inter-individual plane, the shared cognition approach focuses on the social
plane, where emergent conceptions are analysed as a group product. For
instance, it has been observed that providing explanations leads to improve
knowledge (Webb 1991). From the 'individualist' perspective, this can be
explained through the self-explanation effect (Chi, Bassok, Lewis, Reimann &
Glaser, 1989). From a 'group' perspective, explanation is not something
delivered by the explainer to the explainee. As we will see in section 5, it is
instead constructed jointly by both partners trying to understand each other
The idea that a group forms a single cognitive system may appear too
metaphorical to a psychologist. It does not surprise a computer scientist. While
the natural scale for a psychological agent is a human being, the scale of a
computational agent is purely arbitrary. The (vague) concept of agent is used
to represent sometimes a single neurone, a functional unit (e.g., the 'edge
detector' agent), an individual or even the world. The granularity of a
distributed system, i.e., the size of each agent, is a designer's choice. It is a
variable that the designer can tune to grasp phenomena that are invisible at
another scale. It supports systems with different layers of agents with various
scales, wherein one may compare communication among agents at level N and
communication among agents at level N+1. Dillenbourg and Self (1992) built
a system in which the same procedures are used for dialogue among agents
and for each agent's individual reasoning. Hutchins (1991) reports a two-layer
system wherein he can tune communication patterns among the units of an
agent (modelled as a network) and the communications among agents.
According to the respective strengths of intra-network and inter-network links,
he observes an increase or a decrease of the group confirmation bias which
cannot be reduced to individuals’ contributions. Gasser (1991) insists on
properties of multi-agent systems which "will not be derivable or
representable solely on the basis of properties of their component agents" (p.
3. Empirical Issues: effects, conditions and interactions
Not surprisingly, the different theoretical orientations we have just outlined
have tended to employ rather different research paradigms. Generally, socio-
cognitive experiments concerned two subjects of approximately the same age
(or the same developmental level) while the Vygotskian setting involved
adult-child pairs. Moreover, the Piagetian and Vygotskian paradigms used
different collaborative tasks. We come back to these differences later. Other
paradigms have been used independently of a particular theoretical
framework, for instance the 'reciprocal teaching' paradigm (Palincsar and
Brown, 1984; Palincsar, 1987; Riggio et al., 1991) in which one learner plays
the teacher’s role for some of the time and then shift roles with the other
learner. We can also distinguish empirical work according to the size of the
groups involved (dyads versus larger groups) or ways in which mediating
technologies are employed, as in computer-supported collaboration.
There are also differences between the various approaches in terms of the
research methods employed. In the socio-cognitive perspective, the
methodology was to set up conditions hypothesised to facilitate learning and
to compare the outcomes of this intervention with some control group. With
such methods, collaboration is treated as a black box; the focus is on
outcomes. In contrast, research from a socio-cultural point of view tends to
employ micro genetic analyses of the social interaction. The focus is on the
processes involved in social interaction. This is partly because of the
importance attached to the concept of mediation in socio-cultural theory.
Evidence is sought from dialogue for symbols and concepts which mediate
social activity and which can in turn be subsequently found to mediate
individual activity. The shared cognition approach obviously also favours the
Despite their intertwining, we have attempted to disentangle the different
research paradigms and theoretical approaches. In what follows we describe
the ‘evolution’ of empirical research within three paradigms that differ with
respect to the number and the type of variables that are taken into account.
3.1 The "effect" paradigm
Experiments conducted to answer the question “is collaborative learning more
efficient than learning alone?” were fairly straightforward. The independent
variable was 'collaborative work' versus 'work alone'. The choice of the
dependent measures varied according to what the investigators meant by 'more
efficient'. The most frequent measure was the subject’s performance when
solving alone the task they previously solved with somebody else. Some
researchers decomposed this dependent variable into several other measures of
performance, such as the improvement of monitoring and regulation skills
(Brown & Palincsar, 1989; Blaye & Chambres, 1991) or a decrease in the
confirmation bias. Within this paradigm, the precise analysis of effects is the
only way to understand the mechanisms that make collaborative learning
This kind of research let to a body of contradictory results, within which the
positive outcomes largely dominate (Slavin, 1983; Webb, 1991). Nevertheless,
negative results cannot always be discarded as the result of experimental
errors or noise. Some negative effects are stable and well documented, for
instance the fact that low achievers progressively become passive when
collaborating with high achievers (Salomon and Globerson,1989; Mulryan,
1992). There is a simple way to understand the controversial effects observed
with the first paradigm: collaboration is in itself neither efficient or inefficient.
Collaboration works under some conditions, and it is the aim of research to
determine the conditions under which collaborative learning is efficient. This
brings us to the second paradigm.
3.2 The "conditions" paradigm
To determine the conditions under which collaborative learning is efficient,
one has to vary these conditions systematically. While the first experimental
approach (in very general terms) varies only in terms of the dependent
measures, the second experimental approach varies along two dimensions,
both dependent and independent variables. Numerous independent variables
have been studied. They concern the composition of the group, the features of
the task, the context of collaboration and the medium available for
communication. The composition of the group covers several other
independent variables such as the number of members, their gender and the
differences between participants. It is not possible here to give a complete
overview of the findings concerning each of these variables. We will illustrate
the work with three examples.
3.2.1 Group heterogeneity
Group heterogeneity is probably the most studied variable. Scholars have
considered differences with respect to general intellectual development, social
status or domain expertise. They have considered objective and subjective
differences in expertise (whether the subjects are actually different or just
believe themselves to be so). We restrict ourselves here to objective
differences between the task knowledge of each subject, a parameter which is
relevant for DAI. For the socio-constructivist, this difference provides the
conditions for generating socio-cognitive conflict. For the socio-cultural
approach, it provides conditions for internalisation. However, the nature of
differences differs within each theoretical approach. Socio-cognitive theory
refers to symmetrical pairs (i.e., symmetrical with respect to general
intellectual or developmental level) where members have different viewpoints,
whilst socio-cultural theory is concerned with asymmetric pairs where
members have different levels of skill. Piaget (1965) argued that interaction
with adults leads to asymmetrical power relations or social status, and that in
such interactions adults or more capable children are likely to dominate. The
pressure to conform in the presence of someone with higher perceived status is
not likely, in this view, to lead to genuine cognitive change. Nonetheless,
Rogoff (1990) notes that many studies from a Piagetian perspective have
involved pairing, for example, conservers with non-conservers. This is hardly
pairing children of equal intellectual ability and is more consistent with the
Vygotskian position. The point of difference between the two approaches then
is not one of “equal” versus “unequal” pairs, but exactly what this equivalence
entails. Researchers have attempted to determine the optimal degree of
differences. If it is too small, it may fail to trigger interactions. If the
difference is too large, there may be not interaction at all. For instance, in a
classification task, Kuhn (1972) shown children solutions reflecting a
difference of -1, 0 , +1 or +2 levels compared to their own solutions. He only
observed significant improvement in the +1 condition. This notion of optimal
difference also emerges in DAI where Gasser (1991) notes that agents need a
common semantics even to decide that conflict exists! The 'zone of proximal
development' defines an optimal difference in an indirect way, i.e. not as a
difference between subjects A and B, but as a difference between how A
performs alone and how A performs with B's assistance.
Heterogeneity is also function of the size of the group. Empirical studies
showed that pairs are more effective than larger groups, but heterogeneity is
not the only factor that intervenes. Groups of three are less effective because
they tend to be competitive, whilst pairs tend to be more cooperative
(Trowbridge, 1987). However, differences between group sizes seem to
disappear when children are given the opportunity to interact with other in the
class (Colbourn & Light, 1987).
3.2.2 Individual prerequisites
A second set of conditions defines some prerequisites to efficient
collaboration. It seems that collaboration does not benefit an individual if he
or she is below a certain developmental level. We consider here the absolute
level of the individual, not his or her level relative to the other group
members. According Piaget, for a conflictual interaction to give rise to
progress, it must prompt individual cognitive restructuring. This implies that a
resolution of conflict which would be exclusively based on social regulations
(compliance from one partner for instance) would prevent interaction from
being efficient. Piaget’s theory predicts that pre-operational children lack the
ability to decentre from their own perspective and therefore benefit from
collaborative work. Indeed, as others have noted (Tudge & Rogoff, 1989),
Piagetian theory in this respect leads to something of a paradox. It is not clear
whether social interaction leads to the decentration necessary to benefit from
collaboration, or that decentration has to happen before genuine collaboration
can take place. Other research suggests that developmental factors need to be
taken into account in resolving this issue. Azmitia (1988) looked at pairs of 5
year old with equivalent general abilities and found that when novices (with
respect to the domain) were paired with experts on a model building task they
improved significantly, whilst equal ability pairs did not. Azmitia argues that
pre-schoolers may lack the skill to sustain discussions of alternative
Vygotskian theory does not place the same sort of explicit developmental
constraints on the ability to benefit from collaboration, but recent researchers
(e.g., Wood et al., in press; Tomasello et al., 1993) have argued that certain
skills in understanding other people’s mental states are required for this which
may set developmental constraints on collaborative learning. With a simple
task this may be achievable at around 4 years of age, since children at this age
can understand that another may lack the knowledge necessary to perform an
action (or misrepresent the situation) and they can predict the state of the
other’s knowledge. However, with more complex tasks, which demand
reasoning using that knowledge to predict the partner's actions on the basis of
their belief and intentions may not be achievable until about 6 years. In order
to achieve shared understanding in a collaborative activity, the child must also
be able to coordinate all these representations and have sufficient skills to
communicate with respect to them.
Research on peer tutoring has identified some conditions which are also
relevant to collaborative learning. The first condition is that the child-tutor
must be skilled at the task. Radziszewska and Rogoff (reported in Rogoff,
1990) found that training a 9 year old peer to the same level of performance as
an adult on a planning task led to peer dyads performing as well as adult-child
dyads and better than peer dyads in which neither partner had been trained. A
second pre-requisite is the ability of the child to reflect upon his or her own
performance with respect to the task. Thirdly, in order to tutor contingently
(i.e., to monitor the effects of previous help on subsequent actions by the
learner), the child has to be able to assess whether the learner’s action was
wrong with respect to the instructions or wrong with respect to the task, and
then be able to produce the next tutorial action on the basis of both a
representation of the previous instruction and an evaluation of the learner’s
response to that instruction. Ellis and Rogoff (1982) found that 6 year old
children were relatively unskilled at contingent instruction compared with
adult tutors. Wood et al. (in press) found that 5 year old peer tutors were
similarly unskilled relative to 7 year old tutors, and that 5 year olds tended to
have difficulty inhibiting their own actions sufficiently to allow their “tutee”
to learn the task. However, children at this age were better “collaborators”
than 3 year old comparison dyads.
3.2.3 Task features
Tasks that have been typically used in collaborative learning from a
Vygotskian perspective include skill acquisition, joint planning, categorisation
and memory tasks. In contrast, the implication from socio-cognitive theory is
that tasks should promote differences in perspectives or solutions. Typically,
conservation and coordination tasks involve perspective-taking, planning and
problem solving. There is thus little overlap in the nature of tasks investigated
from the Piagetian and Vygotskian perspective. It is also clear that the nature
of the task influences the results: one cannot observe conceptual change if the
task is purely procedural and does not involve much understanding;
reciprocally one cannot observe an improvement of regulation skills if the task
requires no planning. Some tasks are less “shareable” than others. For
instance, solving anagrams can hardly be done collaboratively because it
involves perceptual processes which are not easy to verbalise (if they are open
to introspection at all). In contrast, some tasks are inherently distributed, either
geographically (e.g., two radar-agents, receiving different data about the same
aeroplane), functionally (e.g., the pilot and the air traffic controller) or
temporally (e.g., the take-off agent and the landing-agent) (Durfee et al.,
3.2.5 Interactions between variables
Researchers rapidly discovered that the independent variables we have
described so far do not have simple effects on learning outcomes but interact
with each other in a complex way. Let us for instance examine the interaction
between the composition of the pair and the task features. Studies that have
compared the relative benefits of interacting with adults versus interacting
with peers suggest that they vary according to the nature of the task, with peers
being more useful than adults in tasks which require discussion of issues.
Adult-child interaction may be more controlled by the adult rather than being a
reciprocal relationship. Children are more likely to justify their assertions with
peers than with adults. Rogoff (1990) notes that the differences between socio-
cognitive and socio-cultural approaches with respect to composition of dyads
are reconcilable. As she points out, whilst Vygotsky focused on acquiring
understanding and skills, Piaget emphasised changes in perspectives or
restructuring of concepts. Tutoring or guidance may be necessary for the
former, whilst collaboration between peers of equivalent intellectual ability
may be better in fostering the latter (Damon, 1984). So, how dyads or groups
should be composed with respect to skills and abilities may depend upon what
learning outcomes one is interested in (e.g., skill acquisition vs. conceptual
change) and what tasks are involved (e.g., acquiring new knowledge versus
restructuring existing knowledge).
Although few studies have involved a direct comparison of peer collaboration
and peer tutoring with the same task, the type of task may interact with the
developmental level of the learner and the nature of the dyad. For example,
Rogoff (1990) argues that planning tasks may be difficult for very young
children because they require reference to things which are not in the “here-
and-now”. However, adults may be able to carry out such metacognitive or
metamnemonic roles that are beyond children, whilst demonstrating to the
child how such processing could be accomplished. So, certain types of task
may have inherent processing constraints which in turn place constraints on
how the interaction should be supported.
3.3 The "interactions" paradigm
The complexity of the findings collected in the second paradigm led to the
emergence of a third one. This introduces intermediate variables that describe
the interactions that occur during collaboration. The question "under which
conditions is collaborative learning efficient?" is split into two (hopefully
simpler) sub-questions: which interactions occur under which conditions and
what effects do these interactions have. The key is to find relevant
intermediate variables, i.e., variables that describe the interactions and that can
be empirically and theoretically related to the conditions of learning and to
learning outcomes. This methodology however raises interpretation
difficulties: if some types of interactions are positively correlated with task
achievement, it may be that such interactions influence achievement or,
conversely that high achievers are the only subjects able to engage these type
of interaction (Webb, 1991). Nevertheless, underlying this approach is a
fundamental shift: it may be time to stop looking for general effects of
collaboration (e.g., in global developmental terms) and focus instead on more
specific effects, paying attention to the more microgenetic features of the
interaction. We will illustrate this viewpoint by two examples that are
important both in psychology and in DAI: explanation and control.
One way of describing interactions is to assess how elaborated is the help
provided by one learner to the other. This level of elaboration can be
considered as a continuum which goes from just giving the right answer to
providing a detailed explanation. Webb (1991) performed a meta-analysis of
the research conducted on this issue. This synthesis lead to two interesting
results: elaborated explanations are not related to the explainee's performance,
but they are positively correlated with the explainer's performance. Webb
explains the first result by the fact that learning from receiving explanations is
submitted to several conditions which may not be watched by the explainer,
e.g., the fact the information must be delivered when the peer needs it, that the
peer must understand it and must have the opportunity to us to solve the
problem. The second result, the explainer's benefit, has been observed by other
scholars (Bargh and Schul, 1980). Similar effects (called the self-explanation
effect) have been observed when a learner is forced to explain an example to
himself (Chi, Bassok, Lewis, Reimann & Glaser, 1989). A computational
model of the self-explanation process have been proposed by VanLehn &
Jones (1993). The main principle is that the instantiation of general knowledge
with particular instances creates more specific knowledge, a mechanism that
has also been studied in machine learning under the label 'explanation-based
learning' (Mitchell et al., 1986). It would nevertheless be a mistake to consider
self-explanation and explanation to somebody else as identical mechanisms.
This would dramatically underestimate the role that the receiver plays in the
elaboration of the explanation. As we will see in section 5, an explanation is
not a message simply delivered by one peer to the other, but the result of joint
attempts to understand each other. Webb (1991) found that non-elaborated
help (e.g., providing the answer) is not correlated with the explainer's
performance and is negatively correlated with the explainee's performance in
the case where the explainee actually asked for a more elaborated explanation.
Webb explains these results by the fact that providing the answer while the
student is expecting an explanation does not help him or her to understand the
strategy, and may lead the explainee to infer an incorrect strategy or to lose his
or her motivation to understand the strategy.
These findings partially answer the second sub-question of this paradigm, the
relationship between categories of interaction and learning outcomes. The first
sub-question concerns the conditions in which each category of interaction is
more likely to occur. Webb (1991) reviewed several independent variables
concerning group composition, namely, the gender of group members, their
degree of introversion or extraversion and their absolute or relative expertise.
With respect to the latter, explanations are more frequent when the group is
moderately heterogeneous (high ability and medium ability students or
medium ability and low ability students) and when the group is
homogeneously composed of medium ability students. Some other group
compositions are detrimental to the quality of explanations: homogeneous
high ability students (because they assume they all know how to solve the
problem), homogeneous low ability groups (because nobody can help) and
heterogeneous groups comprising high, medium and low ability (because
medium ability students seem to be almost excluded from interactions).
Verba and Winnykamen (1992) studied the relationship between categories of
interactions and two independent variables: the general level of ability and the
specific level of expertise. In pairs where the high ability child was the domain
expert and the low ability child the novice, the interaction was characterised
by tutoring or guidance from the high ability child. In pairs where the high
ability child was the novice and the low ability child the expert, the interaction
involved more collaboration and joint construction.
Rogoff (1990, 1991) conducted various experiments in which children solved
a spatial planning task with adults or with more skilled peers. She measured
the performance of children in a post-test performed without help. Overall she
found better results with adult-child than with child-child pairs but, more
interestingly, she identified an intermediate variable which explains these
variations. Effective adults involved the child in an explicit decision making
process, while skilled peers tended to dominate the decision making. This was
confirmed by the children who collaborated with an adult; those who scored
better in the post-test were those for which the adults made the problem
solving strategy explicit. These results are slightly biased by the fact that the
proposed task (planning) is typically a task in which metaknowledge plays the
central role. A socio-cultural interpretation would be that the explication of the
problem solving strategy provides the opportunity to observe and potentially
internalise the partner's strategy. From a socially shared cognition viewpoint,
one could say that making the strategy explicit is the only way to participate in
each other's strategy and progressively establish a joint strategy.
4 Tools for observing interactions
When collaboration is mediated via a computer system, the design of this
system impacts on the collaborative process. This mediation has
methodological advantages: the experimenter may have explicit control over
some aspects of collaboration (e.g., setting rules for turn taking, determining
the division of labour or distribution of activities). The effects of the computer
as medium also has pedagogical aspects: to support the type of interactions
that are expected to promote learning. We describe three settings in which the
computer influences collaboration..
4.1 Two human users collaborate on a computer-based task
Until relatively recently, one of the main advantages associated with computer
use in schools was seen in terms of the potential for individualised learning.
However, since schools generally have more students than computers, children
often work in groups at the computer. Several empirical results suggest that
group work — at least dyadic work — at the computer may enhance the
benefit derived from the collaborative learning situation (for a review, see
Blaye et al, 1990). The specific questions to be addressed here deal with the
extent to which learner(s)-computer interaction and human-human interaction
can reciprocally enhance one another. For instance, interfaces which induce a
specific distribution of roles between learning partners help to foster social
interaction (O'Malley, 1992; Blaye et al., 1991). Such interfaces can serve to
scaffold the executive and regulative aspects of the collaborative task. Another
interesting example concerns the principle of immediate feedback which was
seen as a critical feature in the first generation of educational software. It
seems that immediate feedback may prevent fruitful exchanges between
human co-learners because they then rely on the system to test their
hypotheses instead of developing arguments to convince one another (Fraisse,
1987). In other words, aspects of the software can modify the socio-cognitive
dynamics between the learning partners. In particular, the computerised
learning environment constitutes in itself a mediational resource which can
contribute to create a shared referent between the social partners (Roschelle &
Teasley, in press).
This research does not aim to build a ‘theory' of human-human collaboration
at the computer. The fact that the medium (i.e., the computer) is similar is by
no means a sufficient reason to unify this field of research. Different
interfaces, different computer-based tasks and activities may yield very
different interactions and learning outcomes. However, for the sake of
simplicity, we refer generically to computer-based activities in order to discuss
the other general parameters which exert an influence (e.g., frequency of
feedback, representations induced by the interface, role distribution, etc.).
4.2 Computer-mediated collaboration
While the previous setting was influenced by research in educational
technology, the setting considered here has developed in parallel with work on
'computer-supported cooperative work' (CSCW). This discipline covers
communication systems from simple electronic mail to more advanced
‘groupware’ (Shrage, 1992). There are various ways in which computers can
support communication. In the past, this technology has been restricted to
textual communication, but developments in broad bandwidth technology
allow for more exciting possibilities such as synchronous shared workspaces
and two-way audio-visual communication. Generally speaking, broad
bandwidth is expected to afford greater opportunities for collaboration. This
does mean that older technologies should be superseded. For instance,
asynchronous text-based communication provides time for reflection on
messages and allows students lacking in confidence to learn nevertheless by
“eavesdropping” on conversations. In addition, low bandwidth communication
may have some advantage in that, if it takes time and costs money in terms of
connect time and if displays are restricted to a screen at a time, students may
be forced to consider their responses more carefully.
Computer-mediated communication settings enables the experimenter to
consider the communication bandwidth as factor. For instance, Smith et al.
(1991) observed that task distribution was easier with a larger bandwidth (i.e.,
when seeing each other via video instead of audio-only communication) and
when the setting gave users the feeling of being side-by-side, through having a
shred workspace. They also observed that establishing face-to-face contact
seems to be important during reflection stages, e.g., when partners discuss
their observations, hypotheses or strategies. This fits in with research on
mediated communication which, in general, suggests that face-to-face
communication is more effective than audio-only communication for tasks
which involve elements of negotiation (see Short, Williams & Christie, 1976).
4.3 Human-computer collaborative learning
Human-computer collaboration refers to situations where the system and the
human user share roughly the same set of actions. We don't include systems
which support an asymmetric task distribution, as between a user and a word
processor, for instance. We describe two types of system where some learning
is supposed to result from collaborative activities: apprenticeship systems and
learning environments. Most of these systems do not actually fully satisfy the
An apprenticeship system is an expert system that refines its knowledge base
by watching a human expert solving problems. The human expert is actually
more teaching the system than collaborating with him or her, but the
techniques developed are relevant to collaborative learning. The expert's
behaviour is recorded as an example and the system applies explanation-based
learning (EBL) techniques to learn from this example. In ODISSEUS
(Wilkins, 1988), the system attempts to explain each human action in order to
improve the HERACLES-NEOMYCIN knowledge base. An explanation is a
sequence of metarules that relate the observed action to the problem-solving
goal. If ODISSEUS fails to produce the explanation, it tries to "repair" its
knowledge base by relaxing the constraints on the explanation process. LEAP
(Mitchell et al., 1990) applies a similar approach to the design of VLSI
circuits. The user can reject the proposed solution and refine the circuit him or
herself. In this case, LEAP attempts to create rules that relate a given problem
description to the circuit specified by the expert-user. LEAP explains why the
circuit works for the given input signal and then generalises the explanation to
create the rule premises. The interesting aspect is that these systems attempt to
acquire the metaknowledge used by an expert, a central issue in the
Vygotskian approach. However these systems rely on EBL techniques which
requires a complete theory of the domain. Human learners theories are rarely
complete and consistent. Some research has been carried out to by-pass this
problem by integrating EBL with analogical and inductive learning (Tecuci
and Kodratoff, 1990).
Not surprisingly, the idea of human-computer collaborative learning has also
been applied to educational software. It has firstly been suggested as an
alternative technique for student modelling (Self, 1986), then as an attempt to
break the computer omniscience that dominates educational computing
(Dillenbourg, 1992). An interesting issue concerns the necessity to have a
plausible co-learner. Along the continuum of design choices, we can
discriminate levels of 'sensitivity'. At the first level, we could imagine an
ELIZA-like system which randomly asks questions in order to involve the
learner in plausible collaborative activities. Second level systems include a co-
worker, i.e., an agent which solves problems during the interaction but which
is not learning. For instance, the Integration Kid (Chan & Baskin, 1988) does
not learn, but jumps (an the tutor's request) to the next pre-specified
knowledge level. At the third level, we have a real co-learner, i.e., a learning
algorithm whose outputs are determined by its activities with the world,
including its interactions with the human learner (Dillenbourg & Self, 1992).
This research has not yet produced enough empirical data to determine
whether more sensitive systems are more efficient than less sensitive one.
Another interesting issue to be addressed here is that the phenomena observed
in human-human collaboration are repeated in human-computer collaboration.
Salomon (1990) raises an important point in terms of knowing whether
human-computer interaction has potential for internalisation similar to human-
human conversations. He suggests (Salomon, 1988) that some graphic
representations could have this potential. We observed (Dillenbourg, in press)
that learners were not very 'tolerant' with the computer: firstly, they had
difficulties in accepting that the computerised partner makes silly mistakes,
then, when the computer was repeatedly wrong, they stopped making
suggestions altogether. The advantage of human-computer collaborative
systems for the study of collaboration is that the experimenter can tune several
parameters regarding to the pair composition (for instance, the initial
knowledge of the co-partner).
5 Tools for analysing interactions
At the present state of research, it is not clear which theoretical perspective is
most fruitful for analysing interactions, although incidence of socio-cognitive
conflict appears to be limited and restricted largely to Piagetian tasks (Blaye,
1988). However, other researchers have shown that there are benefits in
generating discussions of conflicting hypotheses for domains such as physics
(e.g., Howe et al., 19??). A number of researchers (e.g., Webb, Ender & Lewis
1986; Blaye, Light, Joiner & Sheldon 1991; Behrend & Resnick 1989) have
shown that various interactive measures other than "conflict" have a positive
correlation with learning outcomes. It may be, as Mandl & Renkl (1992)
suggest, that this uncertainty in the field is due to the fact that the Piagetian
and Vygotskian perspectives as they stand are simply too global to allow
proper explanation of the different results. These authors thus argue that "more
local", domain/task-specific theories should be developed. As Barbieri &
Light (1992) point out, "[s]tudies in collaborative learning at the computer
usually do not go into a detailed analysis of interaction …" (p. 200), despite
the fact that it is "… important to analyse the quality of the interaction more
closely." (p. 200).
5.1 Analysis categories
Most researchers have generally used quite global categories of analysis
grouped according to (at least) the following 'oppositions' : (1) social /
cognitive, (2) cognitive / metacognitive, and (3) task / communicative . We
briefly discuss each in turn.
With respect to the social/cognitive distinction, for example, Nastasi &
Clements (1992) distinguish "social conflict" (i.e., not related to the problem,
such as "name calling", "criticism", etc.) from "cognitive conflict" (which
concerns the task conceptualisation or solution). Only the latter was expected
to (and did in fact) have a positive correlation with individual improvement.
In terms of the cognitive/metacognitive distinction, Artzt and Armour-Thomas
(1992) coded "episodes" such as reading, as cognitive, and understanding,
planning and analysing as metacognitive. Several types of episodes such as
"exploring" and "verifying" solutions were categorised as cognitive and
metacognitive. The working hypothesis was that "the most successful groups,
in terms of both solving the problem and getting active involvement of all the
group members, should be those with the highest percentages of metacognitive
behaviors" (Artzt & Armour-Thomas, 1992, p. 165).
The third discrimination is between task and communicative levels. The
communicative level is when the students are trying to achieve a shared
understanding by establishing common referents, by giving "commentaries"
whilst performing actions, for example (Barbieri & Light, 1992). Task-level
analysis categories include "negotiation" (Barbieri & Light, op. cit.), or more
generally "task construction". As with the cognitive/metacognitive distinction,
many analysis categories combine both communicative and (extra-
communicative) task aspects, which is not surprising since the objective is to
study their interrelation. For example, Webb, Ender and Lewis (1986) used
analysis categories that combined simple speech act types (e.g., question,
inform) with parts of the task decomposition (e.g., knowledge of commands,
syntax, etc. in computer programming). In fact, whilst there may exist
utterances in dialogue that are purely concerned with managing the interaction
(such as managing turn-taking, requesting an utterance to be repeated, etc.), in
task-oriented dialogues, most utterances concerning the task also have a
communicative dimension — making a relevant contribution to the task
communicates to the other that you have understood and are sharing a
To summarise, researchers distinguish management of communicational and
social relations from performance of cognitive and metacognitive aspects of
the extra-communicative task. Within these two broad categories, different
forms of conflict are identified. There is, however, a more fundamental
analytical problem to be solved: if individual cognitive progress is associated
with cooperation or collaboration in the interaction, then we need to identify
when students are in fact cooperating or collaborating, and when they are not
really addressing each other (such as "problem-solving in parallel"). This
brings us back to the issue raised in the introduction concerning the theoretical
distinction between cooperation and collaboration. But, the question now is:
how do we know when students are truly collaborating? Which kind of
interactions can be identified as collaborative? In order to address this
question, Roschelle and colleagues introduced the notion of a "Joint Problem
Space" [JPS], consisting of jointly agreed goals, methods and solutions. At the
level of social interaction, in order to determine what is in fact "shared" or
"mutually accepted", it was necessary to determine when a "Yes" signalled
'genuine' agreement and when it merely indicated "turn taking" ("I can hear
you, go on …", etc.). This latter problem has been extensively studied in
linguistics within a general model for linguistic feedback (Allwood, Nivre &
Ahlsén 1991; Bunt 1989). Thus, the meaning of "yes" in a given dialogue
context depends on the preceding speech act (answering "yes" to a yes/no
question is different from responding "yes" to a statement) and the polarity of
the utterance (answering "yes" to "It is raining" may signal acceptance,
whereas "yes" after "It isn't raining" can mean "oh yes it is !" or "yes, I agree
that it isn't raining"). More generally, utterances like "yes", "no", "ok" and
"mhm" give feedback at the level of perception ("I can hear you"),
comprehension ("I can hear and understand you") and agreement/disagreement
("I hear you, understand, and agree"). (The first two of these are generally
referred to as “backchannel” responses which serve to facilitate turn-taking.)
Deciding on the meaning of these expressions in a given dialogue context is
thus quite complex, but necessary if we are to understand when students are
really collaborating and co-constructing problem solutions. At present this line
of research on the pragmatics of communication remains to be exploited in the
field of collaborative learning.
5.2 Conversation models
A promising possibility for collaborative learning research therefore is to
exploit selective branches of linguistics research on models of conversation,
discourse or dialogue to provide a more principled theoretical framework for
analysis. Two types of interaction have been universally referred to in
collaborative learning research : negotiation, often referred to within the
Vygotskian "cooperation" approach as an indicator of joint involvement in
task solutions, and argumentation , as a possible means for resolving socio-
cognitive conflict. In the remainder of this section we review some research in
language sciences and AI which may be relevant to analysing these
interactional phenomena in cooperative problem-solving dialogues.
In the context of joint problem-solving, we can view negotiation as a process
by which students attempt (more or less overtly or consciously) to attain
agreement on aspects of the task domain (how to represent the problem, what
sub-problem to consider, what methods to use, common referents, etc.), and
on certain aspects of the interaction itself (who will do and say what and
when). In DAI, "communication protocols" based on negotiation between
artificial agents have been developed for resolving resource allocation
conflicts (Bond & Gasser, 1988; Rosenschien, 1992). Two main negotiation
strategies may be used : (1) mutual adjustment, or refinement of the positions
of each agent, and (2) competitive argumentation (Sycara 1988,1989), where
one agent attempts to convince the other to adopt his proposition. This
illustrates the fact that quite specific conditions are necessary in order for
negotiation to be used as a strategy: the agents must be able and willing to
relax their individual constraints, and the task must possess the required
'latitude' (if the answer is as clear and determinate as "2+2=4", there is no
space for negotiation) (Adler et al, 1988). Baker (forthcoming) describes the
speech acts and strategies used in collaborative learning dialogues, where a
third strategy (other than refinement and argumentation) is "stand pat" — one
agent elicits a proposal from the other, using the second agent as a "resource".
In other words, we can see at least three different types of negotiation
behaviours, where each may be hypothesised to give different learning
outcomes: (1) co-constructing problem solutions by mutual refinement, (2)
exploring different opposed alternatives in argumentation, and (3) one student
using the other as a resource.
There is, however, another type of negotiation that is common to any verbal
interaction, and which takes place at the communicative, rather than the task,
level: negotiation of meaning. The general idea is that the meaning of
utterances in verbal interaction (or at least, the aspect of meaning that plays a
determining role) is not something that is fixed by speakers and their
utterances, but is rather something to be jointly constructed throughout the
interaction by both speakers. This continuous process of adjustment of
meaning will be a major determinant of what will be internalised at an
individual level. Edmondson (1981) refers to this as "strategic indeterminacy",
meaning that negotiation of meaning is not a 'defect' of interaction, but is
rather constitutive of it to the extent that specific interactive mechanisms exist
that allow mutual understanding to emerge. Thus Moeschler (1985) states that
"Without negotiation the dialogue is transformed into monologue, the function
of the interlocutor being reduced to that of a simple receptor of the message."
(Moeschler, 1985, p. 176). For example, if one speaker (S1) makes the
utterance "the mass is greater for the red ball", and another (S2) replies "No it
isn't", S1 can reply with, "no, no, I wasn't saying it was, it was just
wondering", thus negotiating the illocutionary value of the utterance to be a
question, rather than an affirmation. We can observe this process of
negotiation of meaning most clearly in so called "repair sequences"
(misunderstanding becomes an explicit object of discourse), but it is important
to note that from the point of view of most linguistics schools concerned with
conversation, discourse or dialogue, "negotiation" is not a type of isolated
sequence that may occur in a dialogue, it is a process operating throughout any
dialogue (Roulet, 1992).
Attaining shared understanding of meanings of utterances is a necessary
condition for collaborative activity (one cannot be said to be 'really'
collaborating, or agreed, if one doesn't understand what one is collaborating or
agreed about), and as such the collaborative activity determines the degree to
which 'full' or 'complete' mutual understanding needs to be attained. From a
cognitive perspective, Clark & Shaefer (1989) have expressed this fact in
terms of the speakers' adherence to a criterion of "grounding" : "The
contributor and the partners mutually believe that the partners have understood
what the contributor meant to a criterion sufficient for current purposes"
(Clark & Schaefer, ibid., p. 262). Speakers do this by generating units of
conversation called "contributions". "Contributions" have two phases: a
presentation phase and an acceptance phase. They are recursive structures in
that each acceptance is itself a new presentation, which the hearer is invited to
consider. In acceptance phases, speakers provide evidence of continued
understanding, to a greater or lesser degree. The recursion terminates when
evidence has been provided of the weakest form sufficient for current
purposes at a given level of embedding. Types of evidence provided are
conditional on the adjacency pair which constitutes a contribution. They
include continued attention, initiation of the relevant next contribution,
acknowledgement (feedback or backchannels such as nods, or utterances such
as "uh-huh", "yeah", etc.), demonstration (hearer demonstrates all or part of
what he has understood A to mean), and display (hearer displays verbatim all
or part of speaker's presentation). Contributions may be generated in one of a
number of contribution patterns, such as "contributions by turns", by
"episodes" (corresponding to the "stand pat" negotiation strategy, described
above), and by collaborative completion of utterances. The latter pattern is an
indicator par excellence of collaboration in verbal interactions.
Krauss and Fussell (1991) observed that, during social grounding, the
expressions used to refer to objects tend to be progressively abbreviated
(provided that the partner confirms his or her understanding in the
abbreviation process). Interestingly, the same phenomena of abbreviation is
observed during internalisation (Kozulin, 1990; Wertsch, 1979, 1991), i.e., as
the difference between social and inner speech. This difference is due to the
fact that "inner speech is just the ultimate point in the continuum of
communicative conditions judged by the degree of 'intimacy' between the
addresser and addressee" (Kozulin, 1990, p. 178). These similarities between
social grounding and internalisation fit with the 'distributed cognition' view
that questions the arbitrary boundary between the social and the individual. As
thinking is described as a language with oneself (Piaget, 1928; Vygotsky,
1978), internalisation may be the process of grounding symbols with oneself.
We can ask whether similar grounding mechanisms also occur in human-
computer collaboration. Some experiments with MEMOLAB (Dillenbourg et
al., 1993) revealed mechanisms of human-computer grounding: the learner
perceives how the system understands him and reacts in order to correct
eventual misdiagnosis. Even in DAI, authors start to emphasise the need for
each agent to model each other (Bird, 1993) and exchange self-descriptions
Turning finally to argumentation, we noted above that it is one of the
strategies which may be used in collaborative interactions. As such, the way in
which conflict or disagreement may be resolved in an ensuing argumentation
phase may be strongly influenced by the context of the higher level goal of
achieving agreement. For example, students often take the “least line of
resistance" in argumentation, shifting focus to some minor point on which
they have agreed, and thus never "really" resolving the conflict (Baker 1991).
This may be related to the following question posed by Mevarech and Light
(1992, p. 276): "Is conflict itself sufficient as an "active ingredient", or is it the
co-constructed resolution of such conflict which is effective ?". It therefore
seems clear that detailed analysis of argumentations in collaborative dialogues
may help to give finer-grained indications for explaining some experimental
results. At present, little research has been done on this (but see Trognon &
Retornaz, 1990; Resnick et al, 1991), and a vast literature on argumentation in
language sciences remains to be exploited (this is not the place to review such
a literature, but see for example Toulmin, 1958; Barth & Krabbe, 1982; van
Eemeren & Grootendorst, 1984; Voss et al., 1986; Miller, 1987).
Collaboration is not simply a treatment which has positive effects on
participants. Collaboration is a social structure in which two or more people
interact with each other and, in some circumstances, some types of interaction
occur that have a positive effect. The conclusion of this chapter could
therefore be that we should stop using the word 'collaboration' in general and
start referring only to precise categories of interactions. The work of Webb,
reported above, showed that even categories such as 'explanation' are too large
to be related to learning outcomes. We have to study and understand the
mechanisms of negotiation to a much greater depth than we have so far.
We do not claim that conversational processes are exclusive candidates for
explaining the effects observed. The 'mere presence' of a partner can, in itself,
be responsible for individual progress. Neither should we discard the role of
non-verbal communication in collaboration. However, verbal interactions
probably provide, at present, more tractable ways in which to tackle the
development of computational models of collaborative learning.
In various areas of cognitive science psychologists and computer scientists
have developed computational models together. This is not the case for
collaborative learning. We hope that this chapter will help psychologists and
researchers in machine learning to develop models of collaborative learning.
Both in psychology and in computer science, individual learning and verbal
interactions have been studied separately. The challenge is to build a model
for how the two interrelate, for how dialogue is used as a means for carrying
out joint problem-solving and how engaging in various interactions may
change the beliefs of the agents involved.
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