10
This chapter was originally published as a com= mentary to chapters 3 and 4 of CSCL2: Carrying Fo= rward the Conversation. It is argued that these two examples of leading-ed= ge research in CSCL lose sight of the real phenomena of collaboration due to t= heir use of particular research methodologies that are prevalent in CSCL work. An alternative approach, which analyzes the details of collaborative interacti= ons, is recommended as a supplement to such approaches.
In this essay, I began to reflect on the limita= tions of CSCL methodologies that are derived from related fields, like education = and psychology. It struck me that their drive to quantify data as grist for the statistical mill reduced the richness of the data and eliminated some of the most interesting information for understanding collaboration. While the methodology allowed one to make statistically significant tests of specific hypotheses, it obstructed any attempt to follow the processes proposed in chapter 9’s model. This insight allowed me to anticipate the proposal= of chapter 11 and its implementation in chapters 12 and 13.
The CSCL2= volume (Koschmann, Hall, = & Miyake, 2002) provides a follow-up to the collection that largely defined the field of CS= CL six years earlier (Koschmann, 1996b)= . It compiled a number o= f key papers from the CSCL 1997 conference and was structured with commentaries written in 2001 to spark a knowledge-building conversation within the resea= rch community. The present chapter’s commentary attempted to suggest a methodological turn for the field of CSCL. That suggestion will be further developed and implemented in later chapters of this book.
Chapter 3 of C=
SCL2
was written especially for that edited volume and is entitled
“Computer-supported collaborative learning in university and vocation=
al
education” by Frank P. C. M. de Jong, Else Veldhuis-Diermanse and Gaby
Lugens from the
In the penultimate sentence of their paper, Hakkarainen, Lipponen and Järvelä correctly point out that CSCL researchers have a complex challenge because they “attempt to promote= the educational use of the new information/communication technology while simultaneously trying to implement new pedagogical and cognitive practices = of learning and instruction” (p. 153f). The naïve, technology-driven view was that tools such as CSILE (the software system used in both studies) would, on their own, make a significant difference in the classroo= m. The subsequent experience has been that the classroom culture bends such to= ols 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 both papers focus on the teacher’s role and say little that pertains directly to the role of CSILE, let a= lone to the consequences of specific features of its design.
Moreover, the papers are concerned with exploring the presence of deep knowledge building within groups, as opposed to more superficial exchange of existing personal opinions or individual offerings = of off-the-cuff reactions. Both papers investigate the teacher’s role in making a difference to the depth of collaboration and learning. Again, this= is an important theme for research, but the methodology seems to miss the core phenomenon of interest to CSCL: instances of collaborative learning and det= ails of their computer support.
The two papers have a similar structure: first, they d= iscuss abstract pedagogical issues from the educational or scientific research literature (e.g., the learner-as-thinker or the scientist-as-questioner paradigm). Second, they present a statistical analysis of the notes in spec= ific CSILE databases. Finally, they conclude that certain kinds of individual student = learning took place.
However, in both cases, one could imagine that the same learning might have taken place in these particular classrooms with their particular teacher without any comp= uter support and without any collaboration! While there is no doubt that the concerns expressed and supported in these papers are of vital importance to CSCL research, one wonders what happened to the computer-supported collaboration in CSCL.
The high-level concern of these pa= pers, which ends up ignoring the roles of both collaboration and technology, plays itself out at a methodological level. To see this requires reviewing= the analysis undertaken in these papers.
The paper by de Jong, Diermanse and Lutgens raises three central questions for CSCL environments such as = CSILE:
=
1. Can
these environments be integrated into curriculum at the university level?
=
2. Does
their use promote knowledge building? =
3. What
should the role of the teacher be? Each of these questions would require a book to answer=
with
any completeness—assuming one knew the answers. Research today is rea=
lly
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 be=
ing
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 paper was: in what
cultural and educational setting could a program like =
CSILE be integrated=
?
The studies presented here took place in the A second aspect of context is: at what educational lev=
el is CSILE=
effective? The paper 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=
paper
are not contrasted with other age groups and there is no reason given to th=
ink
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 t=
he
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 ent=
ered
into CSILE.
This was the only significant correlation involving learning styles. This m=
ay
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=
212;not
a very surprising result. A fourth aspect involves the incorporation of collabor=
ation
software into a particular curriculum or classroom culture. As the paper ma=
kes
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 do the teacher and student roles have to be rethou=
ght,
but the curricular goals and the institutional framework need to be as well=
. 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 paper’s conclusion 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 sent=
ence
is made to draw out the contrast. Clearly, by definition, a course that has
been restructured to centrally include collaborative discussion will at lea=
st
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 buildi=
ng
comes from an activity that apparently took place outside of CSILE=
in a non-collaborative manner: the rewriting of educational policy notes. T=
his
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&#=
8221;
this knowledge to the final individual “authentic” task. Thus, =
the paper
concludes that the use of CSILE “resulted in sufficient transf=
er
of the acquired understanding to work within an authentic problem.” T=
here
is no evidence of learning or transfer other than a general judgment that t=
he
final product was of “high quality.” The remaining evidence for collaborative knowledge bui=
lding
is given by two standard statistical measures of online discussions. The fi=
rst
measure is a graph of the number of notes posted by students and teachers d=
uring
each week of the course. 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 a minimal reading of the notes occurs during that time.
This is extraordinary, given that the paper calls this period the “kn=
owledge
deepening phase.” This is precisely when one would hope to see
collaborative knowledge building taking place. As students read, research a=
nd
deepen their ideas they should be sharing and interacting. Clearly, they kn=
ow
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, as this is an all too common finding in CSCL researc=
h.) The vocational study also presents a graph of the numb=
er of
notes posted each week. Here, there are peaks in the middle of the course. =
But,
as the paper 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, su=
ch a
question cannot be addressed. The second statistical measure for the university stud=
y is a
table of correlations among several variables of the threaded discussion: n=
otes
created, notes that respond to earlier notes, no=
tes
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 figure 2 of the study. Here one
student, Elske, has posted a statement of her theory. 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 abo=
ut
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 tak=
ing
place. The paper ends with some important hints about ho=
w CSILE=
classrooms need to be different from lecture-dominated contexts: The use of=
the
collaboration technology must be highly structured, with a systematic didac=
tic
approach, continuing teacher involvement and periodic face-to-face meetings=
to
trouble-shoot problems and reflect on the learning process. These suggestio=
ns
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 classr=
oom
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 paper 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 in uncovering the quality of the discussion that takes place. In th=
eir
discussion section, the authors are clearly aware of the limitations of the=
ir
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 “episte=
mology
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 The Cultural, Educational, Learning and Pedagogical Context
Promoting Collaborative
The Teacher’s Role
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 the extent of the teacher’s interactions with students via CSILE. Thus,= the approach of coding ideas achieved the authors’ goal of showing the im= portance of the classroom culture in determining the character of collaborative knowledge building.
Hakkarainen et al. review certain philosophers of science and characterize the enterprise of science in terms of posing speci= fic kinds of questions and generating particular kinds of statements. This may = be a valid conceptualization of scientific inquiry, but let us consider a differ= ent perspective more directly related to collaboration and computer support.
In his reconstruction of the Origins of the Modern Mind, Donald (1991) locates the birth of sc= ience 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 buildi= ng 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. Similarly, we can look at the effects of the affordances of technologies for expressing, communicating, relating, organizing and retain= ing these evolving ideas.
Unfortunately, Hakkarainen et al. focus exclusive=
ly on
individual statements. They relate their categorization of statements to
For purposes of analyzing the use of CSILE in dif= ferent 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 use= ful approach for deriving qualitative and quantitative answers to certain quest= ions 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 = could not differentiate different kinds of notes from each other at all.
However, the reduction of a rich discussion in a datab= ase 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 acts of communication, interac= tion and knowledge building within a complexly structured community of learners&= #8212;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 experient= ial learning, CSCL research (by no means just the paper under review here, but = most of the best in the field) remains predominantly fact-reductive.
Of course, the methodology of coding statements is use= ful 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 long= er see what scientific theories have been developed or what critical issues ha= ve been raised, but 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 the= ir (and de Jong’s) measures capture only a small part of what has taken place in the classrooms. In their paper they are just trying to make a sing= le focused point about the impact of the teacher-created classroom culture upon the scientific level of the CSILE-mediated discourse. Furthermore, in their discussion section they note the need for different kinds of analysis= to uncover the “on-line interactions between teacher and students” that form a “progressive discourse,” which is central to knowle= dge building according to Bereiter (2002). For future work, they propose social network analysis, which graphically represents who interacted with whom, revealing groups of collaborators and non-collaborators. Althoug= h 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 hea= ds of individual students and then seek relations among these ideas, rather th= an seeking knowledge as an emergent property of the collaboration discourse itself.
These two papers represent typical studies of CSC= L. 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 refinemen= t, although without knowing what was said and how ideas evolved through interactions during those processes it is impossible to judge the importanc= e of the collaboration. The second type of analysis codes the semantics of the n= otes in order 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 observatio= ns from actually observing some of the discussion and understanding its conten= t—and that this would be impractical and unscientific.
A major problem that we have just observed with the prevalent CSCL assessment approaches is that they throw out the actual comp= uter-supported collaborative learning along 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 gr= aph or coding scheme, however much those tools may help to illustrate specific parts of the picture. One central question that needs to be seriously addre= ssed has to do with our claim that collaboration is important for knowledge building. We need to ask where is there 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 stimulat= es another’s initial insight or question, one perspective is taken over = by another, a terminological confusion leads to clarification, a set of hypoth= eses congeals into a theory, and a synergistic group understanding emerges thank= s to the power of computer-supported collaborative learning.
Before we had systems such as
Statistical analysis of outcomes has dominated educational research because it was assumed that learning takes place inside people’s heads, and since Descartes it has been assumed that we have = only indirect access to those processes. Much work in the 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 as applied to individual cogniti= on, 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 collaborat= e on learning, they must display to each other enough that everyone can judge wh= ere there are agreements and disagreements, conflicts or misunderstandings, confusions and insights. In collaborating, people typically establish conventional dialogic patterns of proposing, questioning, augmenting, mutua= lly completing, repairing, and confirming each other’s expressions of knowledge. Knowledge here is not so much the ownership by individuals of me= ntal 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 mem= ber of communities of practice (Lave & Wenger, 1991) and to become competent at using their resources (Suchman, 1987), artifacts (Norman, 1993), speech genres (Bakhtin, 1986a) and cultural practices = (Bourdieu, 1972/19= 95). 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 obse= rve the building of knowledge as it takes place. They do not have to rely on statistical analyses of reified outcomes data and after-the-fact reconstructions (interviews, surveys, talk-alouds), which are notoriously suspect.
Koschmann (1999a) pointed out this potent= ial, derived 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 (1986a), a particular spoken or written utterance is meaningful in terms of its references back to preceding utterances and forward to anticipated responses of a projected audience. Th= ese situated sequences of utterances take advantage of conventional or colloqui= al “speech genres” that provide forms of expression that are clearly interpretab= le within a linguistic community. Explicit cross-references and implicit selec= tions of genres mean that sequences of dialogic utterances display adoptions, modifications and critiques of ideas under discussion, providing an intersu= bjectively accessible and interpretable record of collaborative knowledge building.
In order for collaborative learning processes to be vi= sible to researchers, the participant interaction must be available for careful s= tudy and the researchers must be capable of interpreting them appropriately. In = CSCL contexts, learning may take place within software media that not only trans= mit utterances but also preserve them; the information preserved for participan= ts may be supplemented with computer logging of user actions for the researche= rs. If communications cannot otherwise be captured, such as in face-to-face col= laboration, they can be videotaped; the tapes can be digitized and manipulated to aid in detailed analysis. In either case, it may be possible for researchers to ob= tain an adequate record of the interaction that includes most of the information that was available to participants. In face-to-face interaction, this gener= ally 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 (see chapte= rs 12 and 21).
The problem of researchers being capable of appropriat= ely interpreting the interactions of participants is a subtle one, as anthropologists have long recognized (Geertz, 1973). A family of sciences h= as grown up recently to address this problem; these include conversation analy= sis (Sacks, 1992), ethnomethodology (Garfinkel, 1967; Heritage, 1984), video analysis (Heath, 1986), interaction analysis <= !--[if supportFields]> ADDIN EN.CITE <EndNote><Cite><Author>Jordan</Author><Year>1= 995</Year><RecNum>209</RecNum><MDL><REFERENCE_TY= PE>0</REFERENCE_TYPE><REFNUM>209</REFNUM><AUTHORS&g= t;<AUTHOR>Jordan, Brigitte</AUTHOR><AUTHOR>Henderson, Austin</AUTHOR></AUTHORS><YEAR>1995</YEAR><TITLE= >Interaction analysis: Foundations and practice</TITLE><SECONDARY_TITLE>Jour= nal of the Learning Sciences</SECONDARY_TITLE><VOLUME>4</VOLUME><NUMBER>= ;1</NUMBER><PAGES>39-103</PAGES><URL>http://lrs.ed.= uiuc.edu/students/c-merkel/document4.HTM</URL></MDL></Cite&g= t;</EndNote>(Jordan & Hend= erson, 1995) and micro-ethnography (Streeck, 1983). These sciences have ma= de explicit many of the strategies that are tacitly used by participants to display their learning to each other. Researchers trained in these discipli= nes know where to look and how to interpret what is displayed. Researchers shou= ld 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. Collaboration is an intersubjective occurrence and its scientific study req= uires intersubjective confirmation rather than statistical correlations to assure= its acceptability.
If collaborative learning is visible, then why ha= ven’t more researchers observed and reported it? Perhaps the answer is because collaborative knowledge building is so rare today. I have tried to use syst= ems similar to CSILE in several classrooms and have failed to see them used for knowledge buildi= ng (see chapter 6). They may be used by students to express their personal opinions and raise questions but rarely to engage in the kind of ongoing di= alog that Donald (1991) saw as the basis for a theoretic culture, or to engage in the investigation of “conceptual artifacts” (e.g., theories) that Bereiter (2002) identifies as central to knowledge building. Of the five classrooms reviewed in the two papers featu= red here, probably only one of them, a Canadian classroom, advanced significant= ly 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 (see chapter 9). Even simple statistics on thread lengths in threaded discussion systems ADDIN EN.CITE <EndNote><Cite><Author>Guzdial</Author><Year>= 2000</Year><RecNum>164</RecNum><MDL><REFERENCE_T= YPE>0</REFERENCE_TYPE><REFNUM>164</REFNUM><AUTHORS&= gt;<AUTHOR>Guzdial, Mark</AUTHOR><AUTHOR>Turns, Jennifer</AUTHOR></AUTHORS><YEAR>2000</YEAR><TIT= LE>Sustaining discussion through a computer-mediated anchored discussion forum</TITLE><SECONDARY_TITLE>Journal of the Learning Sciences</SECONDARY_TITLE></MDL></Cite><Cite><Au= thor>Hewitt</Author><Year>1999</Year><RecNum>184= </RecNum><MDL><REFERENCE_TYPE>3</REFERENCE_TYPE><= ;REFNUM>184</REFNUM><AUTHORS><AUTHOR>Hewitt, J.</AUTHOR><AUTHOR>Teplovs, C.</AUTHOR></AUTHORS>&l= t;YEAR>1999</YEAR><TITLE>An analysis of growth patterns in computer conferencing threads</TITLE>&= lt;SECONDARY_TITLE>Computer Supported Collaborative Learning (CSCL '99)</SECONDARY_TITLE><PLACE_PUBLISHED>Palo Alto, CA</PLACE_PUBLISHED><PAGES>232-241</PAGES></MDL><= ;/Cite></EndNote>(Guzdial & Tur= ns, 2000; Hewitt & Teplovs, 1999) indicate that communication does not usually continue long enough to get much beyond chatting. Hence, the reviewed papers are correct that the classroom culture and pedagogy are critical, but they = do not go far enough.
It is probably important for researchers to set up spe= cial 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 stu= dies we have just reviewed, despite the fact that teachers in CSILE classr= ooms are explicitly trained to foster collaborative learning. Student activities must be carefully designed that will require collaboration and that will ta= ke 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 a= nd computer support. Very few notes were entered into the computer system duri= ng the long “knowledge-deepening phase” when students were reading. Perhaps through a different definition of tasks, the students would have us= ed the system more while they were building their knowledge by collecting rele= vant 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 (1996). He designed a series of
tasks in physics for pairs of students to work on using a computer simulati=
on
of velocity and acceleration vectors. He videotaped their interactions at t=
he
computer and in subsequent interviews. Through word-by-word analysis of the=
ir
interactions, Roschelle was able to observe and interpret their collaborati=
on
and to demonstrate the degrees to which they had or had not learned about t=
he
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 <= span style=3D'mso-bidi-font-family:"Times New Roman"'>CSILE. But c= onversation analysis was originally studied in the context of telephone interactions ADDIN EN.CITE <EndNote><Cite><Author>Schegloff</Author><Year&g= t;1973</Year><RecNum>268</RecNum><MDL><REFERENCE= _TYPE>0</REFERENCE_TYPE><REFNUM>268</REFNUM><AUTHOR= S><AUTHOR>Schegloff, E.</AUTHOR><AUTHOR>Sacks, H.</AUTHOR></AUTHORS><= YEAR>1973</YEAR><TITLE>Opening up closings</TITLE><SECONDARY_TITLE>Semiotica</SECONDARY_TIT= LE><VOLUME>8</VOLUME><PAGES>289-327</PAGES></= MDL></Cite></EndNote>(Schegloff & S= acks, 1973), so it is possible to interpret interactions where bodily displays are exclu= ded. Computer-mediated collaboration will turn out to look quite different from face-to-face interaction, but we should still be able to observe learning a= nd knowledge building taking place by working out the ways in which people make and share meaning across the network. By making visible in our analysis wha= t is already visible to the participants, we can rediscover the collaborative learning and the effects of computer support in CSCL contexts.