Rediscovering CSCL
Commentary to Chapters 3 and 4. CSCL2: Carrying Forward the Conversation. T. Koschmann, R. Hall,
In their penultimate sentence, Hakkarainen, Lipponen,
and Järvelä correctly point out that CSCL researchers have a complex challenge
because the educational use of new information/communication technologies is
inextricably bound up with new pedagogical and cognitive practices of learning
and instruction. The naïve,
technology-driven view was that tools such as CSILE would make a significant
difference on their own. The subsequent experience has been that the classroom
culture bends such tools to its own interests, and that this culture must be
transformed before new media can mediate learning the way we had hoped they
would. So CSCL research has necessarily and properly shifted from the
affordances and effects of the technology to concerns with the instructional
context. Thus, the central conclusions of Chapters 3 and 4 focus on the
teacher’s role and say little that pertains to the presence of CSILE.
The two chapters have a similar structure: first they
discuss abstract pedagogical issues from the educational or scientific research
literature (e.g., the learner-as-thinker or the scientist-as-questioner
paradigm). Then they present a statistical analysis of the notes in specific
CSILE databases. Finally, they conclude that certain kinds of learning took
place.
However, in both cases, one could imagine that the same
learning might have taken place in these particular studied classrooms with
their particular teacher guidance, without
any computer support and without any collaboration! While there is no doubt
that the concerns expressed and supported in these chapters are of vital
importance to CSCL research, one wonders what happened to the CSCL.
The high-level concern of these chapters, which ends up
ignoring the role of collaboration and technology, plays itself out at a
methodological level. To see this requires reviewing the analysis undertaken in
these chapters.
The chapter by de Jong, Diermanse, and Lutgens raises
three central questions for CSCL environments such as CSILE:
Each of these questions would require a book to answer with
any completeness – if we knew the answers. Research today is really just
starting to pose the questions. Any answers proposed either supply the writer’s
intuitive sense of what took place in an experiment or they rely on a
methodology whose limitations become obvious in the very process of being
applied to these questions. Let us consider each of these questions in turn.
Can CSILE (to use this prototypical system as a
representative of the class of possible software systems for supporting
collaborative knowledge building) be integrated into curriculum? The first
issue implicitly posed by raising this question in the chapter was: in what
cultural and educational setting? The studies presented here took place in the
A second aspect of context is: At what educational level is
CSILE effective? The chapter reports studies at the university level and at a
vocational agricultural school at the same age level. The related European
studies focused on primary school children 9-11 years old. Systems such as
CSILE are most frequently used in primary and middle school classes, although
they are increasingly being used in college classes as well. The studies in
this chapter are not contrasted with other age groups and there is no reason
given to think that educational level makes any significant difference. This is
actually a surprising non-result, because one might assume that collaborative
knowledge building requires mature cognitive skills. It may be that within
modern schooling systems college students have not developed collaborative
inquiry skills beyond an elementary school level.
A third aspect has to do with the learning styles of the
individual students. This issue is explicitly raised by the methodology of the
first (university) study. Here the students were given tests on cognitive
processing strategies, regulation strategies, mental models of learning, and
learning orientation. Based on these scores, they were classified as having one
of four learning styles: application-directed, reproduction-directed,
meaning-directed, or undirected. A statistically significant correlation was
found between the application-directed learners and the number of notes entered
into CSILE. This was the only significant correlation involving learning
styles. This may just mean that students who are generally more inclined to
engage in tasks were in fact the ones who engaged more in the note creation
task of the study – not a very surprising result.
A fourth aspect involves the incorporation of collaboration
software into a particular curriculum or classroom culture. As the chapter
makes clear, CSILE is not intended for a traditional teacher-centered classroom
with delivery of facts through lecture. The use of such a technology as a
centerpiece of classroom learning raises the most complex issues of educational
transformation. Not only the teacher and student roles but also the curricular
goals and the institutional framework have to be rethought. If collaborative
knowledge building is really going to become the new aim, what happens to the
whole competitive grading system that functions as a certification system
integral to industrial society? Is it any wonder that “students are not used to
sharing their knowledge”? What will it take to change this?
The chapter’s conclusion section cites two arguments
for the claim that CSILE resulted in much more collaborative learning by the
students. First, it contrasts the study with “past courses in which students
were directed through the course by closed tasks.” No attempt beyond this half
sentence is made to draw out the contrast. Clearly, by definition, a course
that has been restructured to centrally include collaborative discussion will
at least appear to be more collaborative than its teacher-centered predecessor.
But it is then important to go on and consider concretely what took place
collaboratively and what specific kinds of knowledge were built
collaboratively.
The second evidence for collaborative knowledge building
comes from an activity that apparently took place outside of CSILE in a
non-collaborative manner: the rewriting of educational policy notes. This seems
like precisely the kind of collaborative task that could have pulled the whole
course together as a joint project. Students could have collected and shared
ideas from their readings with the goal of building a group external memory of
ideas that would be used in collectively rewriting the educational policy.
Instead, the individual students had to retain whatever the group learned using
CSILE, combine it with individualized learning from readings, and “transfer”
this knowledge to the final individual “authentic” task. Thus, the chapter
concludes that the use of CSILE “resulted in sufficient transfer of the
acquired understanding to work with in an authentic problem.” There is no
evidence of learning or transfer other than a general judgment that the final
product was of “high quality.”
The remaining evidence for collaborative knowledge building
is given by two standard statistical measures of online discussions. The first
measure is a graph of the number of notes posted each week of the course by
students and by teachers. In the university study, this chart shows a large
peak at the beginning and a smaller one at the end – for both students and
teachers. There is virtually no addition of new notes for the central half of
the course, and only minimal reading of notes occurs then. This is
extraordinary, given that the chapter calls this period the “knowledge
deepening phase.” This is precisely when one would hope to see collaborative
knowledge building taking place. As students read, research, and deepen their
ideas they should be sharing and interacting. Clearly, they know how to use the
technology at this point. If CSILE truly promotes student-directed
collaboration, then why is this not taking place? Raising this question is in
no way intended to criticize anyone involved in this particular experiment, for
this is an all too common finding in CSCL research.
The vocational study also presents a graph of the number of
notes posted each week. Here, there are peaks in the middle of the course. But,
as the chapter points out, the peaks in student activity directly follow the
peaks in teacher activity. This indicates a need for continuing teacher
intervention and guidance. The apparently causal relation between teacher
intervention and student activity raises the question of the nature of the
student activity. Are students just creating individual notes to please the
teacher, or has the teacher stimulated collaborative interactions among the
student notes? Because the graph only shows the number of created notes, such a
question cannot be addressed.
The second statistical measure for the university study is a
table of correlations among several variables of the threaded discussion: notes
created, notes that respond to earlier notes, notes linked to other notes,
notes revised, and notes read by students. The higher correlations in the table
indicate that many notes were responses to other notes and that these were read
often. This is taken as evidence for a high level of collaboration taking place
in CSILE. A nice sample of such collaboration is given in Fig. 2. Here one
student (Elske) has posted a statement of her theory (MT). A discussion ensues,
mostly over three days, but with a final contribution 9 days later. This
collection of 10 linked notes represents a discussion among four people about
Elske’s theory. It might be informative to look at the content of this
discussion to see what form – if any – of knowledge building is taking place.
The chapter ends with some important hints about how
CSILE classrooms need to be different from lecture-dominated contexts: The use
of the collaboration technology must be highly structured, with a systematic
didactic approach, continuing teacher involvement, and periodic face-to-face
meetings to trouble-shoot problems and reflect on the collaborative learning
process. These suggestions are not specific to the studies presented; they
should only surprise people – if there still are any – who think that putting a
computer box in a classroom will promote learning by itself. These are generic
recommendations for any form of learner-as-thinker pedagogy, regardless of
whether or not there is collaboration or computer support.
The chapter by Hakkarainen et al. comes to a similar
conclusion, by a somewhat different, though parallel route. Some of the
preceding comments apply to it as well. But it also represents a significant
advance at uncovering the quality of the discussion that takes place. In their
discussion section, the authors are clearly aware of the limitations of their
approach, but in their actual analysis they too fail to get at the
collaboration or the computer support.
Hakkarainen et al. are interested in the “epistemology of
inquiry” in CSCL classrooms. That is, they want to see what kinds of knowledge
are being generated by the students in three different classrooms – two in
Statistical analysis of the coded ideas provides strong
evidence that the epistemology of inquiry was different in the three
classrooms. In particular, one of the Canadian classrooms showed a
significantly deeper explanatory understanding of the scientific phenomena under
discussion. This was attributed by the authors to a difference in the classroom
culture established by the teacher, including through the teacher’s
interactions with students via CSILE. Thus, the approach of coding ideas
achieved the authors’ goal of showing the importance of the classroom culture
to the character of collaborative knowledge building.
Hakkarainen et al. review certain philosophers of
science and characterize the enterprise of science in terms of posing specific kinds
of questions and generating specific kinds of statements. This may be a valid
conceptualization of scientific inquiry. But let us consider a different
perspective more directly related to collaboration and computer support.
In his reconstruction of the Origins of the Modern Mind, Donald (Donald, 1991) locates the birth of science
in the discovery by the ancient Greeks that “by entering ideas, even incomplete
ideas, into the public record, they could later be improved and refined” (p.
342). In this view, what drives scientific advance is collaboration that is
facilitated by external memory – precisely the promise of CSCL.
Significantly, this framing of scientific knowledge building
focuses on the social process and its mediation by technologies of external
memory (from written language to networked digital repositories). According to
this approach, we should be analyzing not so much the individual questions and
statements of scientific discourse as the sequences of their improvement and
refinement. Relatedly, we can look at the effects of the affordances of
technologies for expressing, communicating, relating, organizing, and retaining
these evolving ideas.
Unfortunately, Hakkarainen et al. focus exclusively on
the individual statements. They relate their categorization of statements to
CSILE in terms of that system’s “thinking types,” which the CSILE designers
selected to scaffold the discourse of a community of learners. However, the
thinking type categories that label statements in CSILE were designed precisely
to facilitate the interconnection of notes – to indicate to students which
notes were responses and refinements of other notes.
For purposes of analyzing the use of CSILE in different
classrooms, the authors operationalize their view of science. They
systematically break down all the notes that students communicated through
CSILE into unit “ideas” and categorize these textual ideas according to what
kind of question or statement they express. This turns out to be a useful
approach for deriving qualitative and quantitative answers to certain questions
about the kind of scientific discussions taking place in the classrooms.
Indeed, this is a major advance over the analysis in de Jong et al., which
cannot differentiate different kinds of notes from each other at all.
However, the reduction of a rich discussion in a database of
student notes into counts of how many note fragments (“ideas”) fall into each
of several categories represents a loss of much vital information. The notes –
which were originally subtle speech acts within a complexly structured
community of learners – are now reified into a small set of summary facts about
the discussion. For all the talk in CSCL circles about moving from
fact-centered education to experiential learning, CSCL research (by no means
just the chapter under review here, but most of the best in the field) remains
predominantly fact-reductive.
Of course, the methodology of coding statements is useful
for answering certain kinds of questions – many of which are undeniably
important. And the methodology can make claims to scientific objectivity:
wherever subjective human interpretations are made they are verified with
inter-rater reliability, and wherever claims are made they are defended with
statistical measures of reliability.
However, it becomes clear here that the coding process has
removed not only all the semantics of the discussion so that we can no longer
see what scientific theories have been developed or what critical issues have
been raised. It has also removed any signs of collaboration. We do not know
what note refined what other note, how long an important train of argument was
carried on, or how many students were involved in a particular debate. We
cannot even tell if there were interactions among all, some, or none of the
students.
To their credit, Hakkarainen et al. recognize that their
(and de Jong’s) measures capture only a small part of what has taken place in
the classrooms. In their chapter they are just trying to make a single focused
point about the impact of the teacher-created classroom culture upon the
scientific niveau of the CSILE-mediated discourse. Furthermore, in their
discussions section they note the need for different kinds of analysis to
uncover the “on-line interactions between teacher and students” that forms a
“progressive discourse,” which is central to knowledge building according to
Bereiter (Bereiter, 2002). For future work they propose
social network analysis, which graphically represents who interacted with whom,
revealing groups of collaborators and non-collaborators. Although this would
provide another useful measure, note that it too discards both the content and
the nature of any knowledge building that may have taken place in the
interactions. Methodologically, they still situate knowledge in the heads of
individual students and then seek relations among these ideas, rather than
seeking knowledge as an emergent property of the collaboration discourse
itself.
Chapters 3 and 4 represent typical studies of CSCL. The
first type provides graphs of note distributions and argues that this
demonstrates computer-supported collaboration that is more or less intense at
different points represented in the graph. Sometimes, additional analyses of
discussion thread lengths provide some indication of processes of refinement,
although without knowing what was said and how ideas evolved through
interactions during that process it is impossible to judge the importance of
the collaboration. The second type of analysis codes the semantics of the notes
to make conclusions about the character of the discussion without really
knowing what the discussion was about. It has generally been assumed that the
only alternative is to make subjective and/or anecdotal observations from
actually observing some of the discussion and understanding its content – and
that this would be impractical and unscientific.
A major problem that we have just observed with the
prevalent assessment approaches is that they throw out the CSCL with the
richness of the phenomenon when they reduce everything to data for statistics.
What we need to do now is to look at examples of CSCL and
observe the collaboration taking place. Collaborative knowledge building is a
complex and subtle process that cannot adequately be reduced to a simple graph
or coding scheme, however much those tools may help to paint specific parts of
the picture. One central question that needs to be addressed seriously has to do
with our claim that collaboration is important for knowledge building. We need
to ask where there is evidence that knowledge emerged from the CSCL-mediated
process that would not have emerged from a classroom of students isolated at
their desks, quietly hunched over their private pieces of paper. Beyond that,
we should be able to trace the various activities of collaborative knowledge
building: where one person’s comment stimulates another’s initial insight or
question, one perspective is taken over by another, a terminological confusion
leads to clarification, a set of hypotheses congeals into a theory, ... , and a
synergistic understanding emerges thanks to the power of computer-supported
collaborative learning.
Before we had systems such as CSILE, collaboration across a
classroom was not feasible. How could all the students simultaneously
communicate their ideas in a way that others could respond to whenever they had
the time and inclination? How could all those ideas be captured for future
reflection, refinement, and reorganization? CSCL promises that this is now
possible. We have to show that it has become a reality in showcase classrooms –
that CSCL systems really do support this and that exciting things really are
taking place thanks to this technology that could not otherwise. Only when our
analyses demonstrate this will we have rediscovered CSCL in our analysis of
classroom experiments.
Statistical analysis of outcomes has dominated
educational research because it was assumed that learning takes place in
people’s heads, and since Descartes it has been assumed that we have only
indirect access to processes in there. Much work in cognitive sciences,
including artificial intelligence, assumes that we can at best model the mental
representations that are somehow formed or instilled by learning. Whatever we
may think of these assumptions, they surely do not apply to collaborative
learning. By definition, this is an intersubjective achievement; it takes place
in observable interactions among people in the world.
The point is that for two or more people to collaborate on
learning, they must display to each other enough that everyone can judge where
there are agreements and disagreements, conflicts or misunderstandings, confusions
and insights. In collaborating, people typically establish conventional
dialogic patterns of proposing, questioning, augmenting, mutually completing,
repairing, and confirming each other’s expressions of knowledge. Knowledge here
is not so much the ownership by individuals of mental representations in their
heads as it is the ability to engage in appropriate displays within the social
world. Thus, to learn is to become a skilled member of communities of practice (Lave & Wenger,
1991)
and to be competent at using their resources (Suchman, 1987), artifacts (Norman, 1993), speech genres (Bakhtin, 1986), and cultural practices (Bourdieu, 1972/1995). The state of evolving
knowledge must be continually displayed by the collaborating participants to
each other. The stance of each participant to that shared and disputed
knowledge must also be displayed.
This opens an important opportunity to researchers of
collaborative learning that traditional educational studies lacked: what is
visible to the participants may be visible to researchers as well. Assuming that
the researchers can understand the participant displays, they can observe the
building of knowledge as it takes place. They do not have to rely on
statistical analysis of reified outcomes data and after-the-fact
reconstructions that are notoriously suspect. Koschmann (Koschmann, 1999) pointed out this potential
deriving from the nature of dialog as analyzed by Bakhtin and also cited
several studies outside of CSCL that adopted a discourse analytic approach to
classroom interactions.
According to Bakhtin (Bakhtin, 1986), a particular spoken or
written utterance is meaningful in terms of its references back to preceding
utterances and forward to responses of a projected audience. These situated
sequences of utterances take advantage of conventional or colloquial “speech
genres” that provide forms of expression that are clearly interpretable within
a linguistic community. Explicit cross-references and implicit selections of
genres mean that sequences of dialogic utterances display adoptions,
modifications, and critiques of ideas under discussion, providing an
intersubjectively accessible and interpretable record of collaborative
knowledge building.
For collaborative learning processes to be visible to
researchers, the participant interaction must be available for careful study
and the researchers must be capable of interpreting them appropriately. In CSCL
contexts, learning may take place within software media that not only transmit
utterances but also preserve them; the information preserved for participants
may be supplemented with computer logging of user actions for the researchers.
If communications are not otherwise captured, as in face-to-face collaboration,
they can be videotaped; the tapes can be digitized and manipulated to aid
detailed analysis. In either case, it may be possible for researchers to obtain
an adequate record of the interaction that includes most of the information that
was available to participants. In face-to-face interaction, this generally
includes gesture, intonation, hesitation, turn-taking, overlapping, facial
expression, bodily stance, as well as textual content. In computer mediated
collaboration, everyone is limited to text, temporal sequence, and other
relationships among distinct utterances – but the number of relevant
interrelated utterances may be much higher. To avoid being swamped with data
that requires enormous amounts of time to analyze, researchers have to set up
or focus on key interactions that span only a couple of minutes.
The problem of researchers being capable of appropriately
interpreting the interactions of participants is a subtle one, as
anthropologists have long recognized (Geertz, 1973). A family of sciences has
grown up recently to address this problem; these include conversation analysis (Sacks, 1992), ethnomethodology (Garfinkel, 1967;
Heritage, 1984),
video analysis (Heath, 1986), interaction analysis (Jordan & Henderson,
1995),
and microethnography (Streeck, 1983). These sciences have made
explicit many of the strategies that are tacitly used by participants to
display their learning to each other. Researchers trained in these disciplines
know where to look and how to interpret what is displayed. Researchers should
also have an innate understanding of the culture they are observing. They
should be competent members of the community or should be working with such
members when doing their observation and analysis. For this reason, as well as
to avoid idiosyncratic and biased interpretations, an important part of the
analysis of interaction is usually conducted collaboratively. At some point,
the interpretation may also be discussed with the actual participants to
confirm its validity. Collaboration is an intersubjective occurrence and its
scientific study requires intersubjective confirmation rather than statistical
correlations to assure its acceptability.
If collaborative learning is visible, then why haven’t
more researchers observed and reported it? Perhaps because collaborative
knowledge building is so rare today. I have tried to use systems similar to
CSILE in several classrooms and have failed to see them used for knowledge
building (Stahl, 1999). They may be used by students
to express their personal opinions and raise questions but rarely to engage in the
kind of ongoing dialog that Donald (Donald, 1991) saw as the basis for a
theoretic culture or to engage in the investigation of “conceptual artifacts”
(e.g., theories) that Bereiter (Bereiter, 2002) identifies as central to
knowledge building. Of the five classrooms reviewed in chapters 3 and 4,
probably only one of the Canadian classrooms advanced significantly beyond the
level of chat to more in-depth knowledge building. The exchange of superficial
opinions and questions is just the first stage in a complex set of activities
that constitute collaborative knowledge building (Stahl, 2000). Even simple statistics on
thread lengths in threaded discussion systems (Guzdial & Turns,
2000; Hewitt & Teplovs, 1999) indicate that communication does not usually
continue long enough to get much beyond chatting. Hence the reviewed chapters
are correct that the classroom culture and pedagogy are critical, but they do
no go far enough.
It is probably important for researchers to set up special
learning contexts in which students are guided to engage in collaborative
knowledge building. Too much of this was left up to the teachers in the studies
we have just reviewed – despite the fact that teachers in CSILE classrooms are
explicitly trained to foster collaborative learning. Student activities must be
carefully designed that will require collaboration and that will take advantage
of computer support for it. For instance, in the Dutch university case it
sounds like the wrong tasks were made the focus of collaboration and computer
support. Very few notes were entered into the computer system during the long
“deepening knowledge phase” when students were reading. Perhaps through a
different definition of tasks, the students would have used the system more
while they were building their knowledge by collecting relevant ideas and facts
in the computer as a repository for shared information. The final product – the
educational policy note – could have been made into the motivating
collaborative task that would have made the collection and analysis of all the
issues surrounding this meaningful.
A nice success story of a researcher setting up a CSCL
situation is related by Roschelle (Roschelle, 1996). He designed a series of
tasks in physics for pairs of students to work on using a computer simulation
of velocity and acceleration vectors. He videotaped their interactions at the
computer and in subsequent interviews. Through word-by-word analysis of their
interactions, Roschelle was able to observe and interpret their collaboration
and to demonstrate the degrees to which they had or had not learned about the
physics of motion. He did the equivalent of looking seriously at the actual
content of the thread of notes between Elske and her fellow students in the
It is true that Roschelle analyzed face-to-face
communication and this is in some ways a richer experience than
computer-mediated interaction using software such as CSILE. But communication
analysis was originally studied in the context of telephone interactions (Schegloff & Sacks,
1973),
so it is possible to interpret interactions where bodily displays are excluded.
Computer-mediated collaboration will turn out to look quite different from
face-to-face interaction, but we should still be able to observe learning and
knowledge building taking place there by working out the ways in which people
make and share meaning across the network. By making visible in our analysis
what is already visible to the participants, we can rediscover the
collaborative learning and the effects of computer support in CSCL contexts.
The view of collaborative learning as visible in
interaction is itself a collaborative product that has emerged in interactions
of the author with Timothy Koschmann, Curtis LeBaron, Alena Sanusi, and other
members of a Fall 2000 seminar in CSCL.
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