Grappling with ideas

h1. Grappling with ideas: Divergence and convergence (paper)

//Paper written for a course at OISE. Based on presentation to CCK11 ([[grappling with ideas|extended notes]]).//

h1. Introduction

We are currently living in a knowledge society, and an ever-increasing
part of the workforce is constituted of “knowledge workers” — how well
we can work with ideas is becoming more and more crucial to a nation’s
competitive advantage. In this paper, I will examine innovative ways of
working with ideas in three different settings, or at three different
levels: individually working with ideas, collaborative group learning in
an online/hybrid course, and workshop methodology in a physical meeting.

I will begin by introducing three different examples of these settings
from my own experience, which began my thinking about this. There is a
copious literature, both about individually working with ideas, and
about group collaborative learning (there is far less about workshop
methodologies), but these theories rarely intersect, or even acknowledge
other possible levels. This paper will examine several of the key
concepts in the literature around individual knowledge management, and
group collaborative learning, as well as my experiences with a specific
workshop methodology, to see if there are commonalities and
intersections between the three levels of engagement with ideas.

h1. Introducing the three settings

In the following sections, I will introduce two specific examples from a
hybrid online class using a novel pedagogical approach coupled with an
innovative technology, as well as a creative way of organizing
workshops. I will also introduce the general challenge of working with
ideas individually, from the point of view of a graduate student. In
future sections, I will introduce some key analytical concepts, and use
them to reflect back on the commonalities and differences in these three
settings or experiences.

h2. Knowledge Forum

One of the first courses I ever took at OISE was Marlene Scardamalia’s
“Knowledge Building for a Knowledge Society”, which she taught using
“Knowledge Forum”, a spatially-organized system for collaborative
discourse (Scardamalia 2003). At the time, I had no background in
education in general, or educational technology and pedagogy in
particular, and this course was different from anything I had ever
experienced.

We met for two hours each week, and between classes we spent time
individually adding content to the Knowledge Forum database. Scardamalia
did not lecture during the two hours we had together — at first she
introduced some of the key concepts, as well as software, then we
discussed the weekly topics quite loosely. Most of the learning happened
asynchronously, in the database.

The course was organized around a draft manuscript written by
Scardamalia and Bereiter about a Knowledge Building Society, and
initially we spent each week reading chunks of text making up one
chapter, and adding our ideas using the build-upon, and the scaffolds.
Already at this stage, I saw how Knowledge Forum let us carry out many
discussions at once, without loosing track, or having some discussions
dominate, while others got bypassed.

But the real power became apparent around week four or five. Some of the
students suggested that once we had finished reading the manuscript, we
step back and reorganize all of our notes according to topics. We all
had the power to create new views, and we were now able to pursue what
had been our initial interests coming into the course, as well as
recognize emerging themes that nobody had expected to see.

By going back through the previous weeks, rereading the discussions, and
copying relevant notes to the new topical views, we were able to get an
overview of “our collective state of knowledge” about for example
Knowledge Building in higher education, or Knowledge Building and
assessment. Seeing the notes next to each other, we could identify gaps,
and keep probing deeper into a given topic.

h2. Workshop

In 2007, I had a chance to attend the Open Translation Tools workshop in
Zagreb, organized by Allen Gunn at Aspiration Tech. His vision, which he
had received Open Society Institute funding for, was to bring together
two groups: people working on open-source translation and linguistic
tools, and people working on projects that needed collaborative
translation. At the end of the workshop, the goal was for the first
group to gain a much better understanding of the needs of people
actually engaged in community translation, the second group to gain a
better understanding of what tools and technical support was available,
and for us as a group to produce some deliverables. These included a map
of existing software, a list of case studies for translation (the
combination of which could be used to identify gaps and needed
software), and a report about the key issues facing collaborative
translation as a field.

Apart from these specific issues, an overarching goal was for strong
social bonds to be formed between participants, enabling future
collaboration and knowledge-sharing, and for the workshop to be run in a
participatory, collaborative and engaging way, where everyone had a
chance to explore their own interests and share their own experiences in
a participatory and democratic way.

After this workshop, I have participated in a number of workshops
organized by AspirationTech, and others inspired by the same workshop
methodology. The methodology is inspired by “unconferences” — a response
to massive conferences where people present on what they submitted
perhaps half a year earlier, with almost all the conversation going in
one direction, very little chance for interaction, and no flexibility in
the program to give space to emergent ideas that might appear during the
workshop. At an unconference, there is no set program, rather people who
appear add ideas for sessions to a large poster, and all the sessions
are discussion-based, rather than lecture-based.

While the unconference format adds excitement, interaction, space for
emergent ideas and much richer social interactions, they are not quite
suitable for workshops that want to “get somewhere” — for unconferences,
the social interactions and open idea-exchanges are a sufficient goal in
themselves. This is where Gunn’s workshop methodology shines. A workshop
typically begins by creating the agenda. Everyone Is asked to come up
with as many ideas as possible about what we are going to discuss during
the next few days, writing each individual idea on a sticky-note, more
than a word, and less than a paragraph (typically a statement or a
question).

All these sticky-notes (the more, the better) are glued onto a large
canvas of butcher-paper covering an entire wall. Since everyone is
working in parallel (sometimes you can also do this in groups of three,
to help people build on each others’ creativity), this can go very
quickly. In ten minutes, you can have generated hundreds of ideas. Then,
participants group the sticky notes on the canvas, all ten to twenty
participants are at the front, finding sticky-notes that are similar and
moving them together. In the end, you have a number of clusters (and
perhaps some outliers). The first session ends with naming the different
groups. These groups will constitute the agenda for the workshop.

When the groups come back after a break, they workshop facilitator has
discussed with the people organizing the conference, and come up with a
number of sessions that address different groups of ideas, sessions that
run in parallel, since small groups are more effective and give each
person more time to talk and listen. They take the sticky notes from
their category with them, and are asked to come back with either some
key findings for the group to reflect on, or a suggestion of future
specific sessions to address these issues more in-depth.

h2. Individually working with ideas

I have been a student for eight years, working with ideas and
information. As a student, you process a large amount of input —
lectures, readings — and somehow need to capture the most important
information, and engage with the ideas contained within, both for the
direct purpose of learning, and for the purpose of producing the many
artifacts that are requested of students in school — presentations,
papers and projects.

As I progress through different levels of higher education, the amount
of input sources increases, together with the rising expectations for
synthesis, critique and creativity. In preparing a PhD research project,
and subsequent thesis, you must go through hundreds of journal articles,
conference presentations and individual discussions, mapping out
relationships between complex ideas, and coming up with new ideas and
insights.

Scholars have always had to grapple with an “information overload”,
Blair (2010) writes about how scholars in the middle ages used tools
such as ‘commonplace books” to collect quotes and excerpts, and there
was a large industry producing printed books of excerpts. However, the
rate of information has increased rapidly — a simple Google Scholar
search can give you full-text access to thousands of nominally relevant
papers. My own exploration of this space was partly prompted by my
experience using a Kindle e-book reader to read the academic literature
for my research.

The Kindle e-book reader enables the user to quickly highlight relevant
passages of the text — these passages are stored in a text file, which
can be imported and manipulated on a computer. After only a few months
or using the Kindle intensively, I had a collection of more than 900
“snippets” from books and articles I had read. I wanted a way of storing
and visualizing these snippets that would help me draw connections, and
engage in deeper thinking around specific topics.

h1. Two cross-cutting concepts

In this section, I will discuss a number of ways in which we work with
ideas, divided into two cross-cutting concepts — externalization and
representation of ideas, and manipulating ideas. I will introduce to
possible interaction scripts for an online course, stimulus/response and
divergence/convergence, and also the concept of granularity of
collaboration.

h2. Externalization

The first step when we are grappling with ideas, is to get those ideas
out of our minds, and down on paper. According to Buzan and Buzan
(2006), putting ideas down on paper helps you realize what you know,
draw connections and propel your thinking forwards. In this section, I
will introduce a few different ways in which externalizing ideas helps
us work with them better, and link these to the three settings
introduced previously.

h3. Formulating an idea

The first step of having to formulate an idea is helpful in itself,
because we have to take something that is a fuzzy representation of
associations and concepts in our mind, and choose explicit words, or
graphics, to represent it on paper, on screen. If we are jotting down,
we might also have to choose where on the page we place each idea, which
font and weight we write it in, etc. All of this aids us in exploring
relationships and hierarchies between our ideas.

h3. Getting around working-memory constraints

It is only possible for us to mentally hold and manipulate a small
number of items in the working memory at a time. This, together with the
lack of clearly formulating an idea, is why we might sometimes mull over
an idea in our heads for a long time, and still feel like we are not
getting any closer.

Getting the ideas out on a piece of a paper, or in a note-taking
software, is a great first step to increasing your capacity to deal with
them. The next step is categorization and hierarchy. You start seeing
patterns in your ideas, and begin to organize them — because a hundred
similar ideas all listed next to each other, are also very difficult to
deal with. By working through them slowly, you combine, sort, move four
different ideas into its own cluster on the page. If you name that
cluster, it now takes one location in your short-term memory, not four.

The program Tinderbox, a very interesting graphical idea organizer
developed by Bernstein (2007), has a concept called yanking, where you
temporarily make a sub-topic in the outline fill the whole screen, and
become the top or central node for a while. The fact that all of your
ideas are “safely stored” on the page, means that you can free yourself
from the burden of having to remember them all, and put the concern for
the whole away for a little bit. You can focus all of your energy on a
small part of the idea map, and be confident in your knowledge that all
of the other ideas have been captured, and will be there when you
return.

This principle is also present in the productivity system “Getting
Things Done” (Allen 2001), which has been adopted widely by people whose
work descriptions entail an ever growing array of different tasks and
projects to coordinate. One of the key recommendations of this system is
to write down everything you need to do, in detail. If you don’t do
this, the brain will continue chewing over things, having you randomly
remember important todos in the shower, during other times when there is
nothing that you can do about it. When things have been written down,
and the brain trusts the system to remind it at the appropriate time,
this frees up brain cycles allowing for much higher focus, creativity
and productivity around the task at hand.

There is an interesting parallel to object-oriented programming (OOP)
practices. Current software projects often extend to tens or hundreds of
thousands of lines of code. It’s unfeasible for anyone to keep an
internal representation of such a large codebase, and when bugs appear,
it is very hard to locate them. Therefore, using OOP, it is possible to
isolate subsections of the code into methods, that have very well
defined data going in, and data coming out. A method can be tested very
thoroughly in isolation, making sure it always does what it should do,
and after that, the programmer can mentally forget the lines of code
that it represents, and just treat it as something that always works,
thus reducing the complexity of his or her internal representation of
the software.

h3. Social benefit of trusting that our ideas are being taken care of

The idea that you trust the system, and the facilitator, with taking
good care of your ideas, and making sure that they will be properly
dealt with, is also a key social concept of both the workshop
methodology, and in a Knowledge Forum class, which does a lot to create
a more positive, open and engaging audience. At a workshop, if you have
expressed your ideas during the initial brainstorming session, and had
that become part of a named category, then you know that it is “safe”,
and that it will be dealt with, and given sufficient time, before the
workshop is over.

Safe in that knowledge, you can put away the attitude which is so common
in traditional meetings, where everyone are jostling to have their point
of view, or idea, discussed, and because of this focus on their own
ideas, are not open enough to listen and engage deeply and honestly with
other people’s ideas.

You find something similar if you compare a class taught in Knowledge
Forum, with a class taught either traditionally as a face-to-face
seminar class, or using traditional threaded online tools. In the two
latter examples, many students are eager to get their point across,
before the topic changes, because there is a sense that once the topic
has changed, there is no way back. However, with a Knowledge Forum
database, multiple different threads of conversation can be maintained
at the same time, with none of them “drowning” any of the others out.

h3. Spatial organization and salience

There are many different ways of representing knowledge, or taking
notes, whether manually on a piece of paper, or supported by an
application. How you organize notes will impact the way you think, and
the patterns that you see emerging, and the design of tools for
collaborative discourse will impact the way the discourse unfolds.

Buzan and Buzan (2006) suggest that the two key factors in increasing
memory retention of notes are association and emphasis. Both of these
are lacking in linear lecture notes. Given that our brains tend to look
for patterns, and completion, structured, spatially organized, and
linked notes can be a very powerful tool for thought, whereas in
traditional notes, the important key concepts drown in a torrent of
text.

Suthers (2001; 2008) has developed this into a theoretical framework for
analyzing how the design and features of a collaborative tool affects
the direction of the discourse, and the outcome of the learning
interaction. He did a series of very interesting experiments, where he
had two students come into his lab, sitting facing each other looking at
individual computers. He would give one person half of the necessary
information, and the other person the other half, and only allow them to
use the computer to communicate. Later, he would analyze the quality of
the finished product, and also call them in after one week to measure
recall.

The two key terms that he came up with, to analyze differences in
systems designs were salience and constraints. A torrent of text, as
Buzan and Buzan (2006) complained about above, has no salient features,
and no constraints. You can write about anything, but nothing stands
out, none of the features of the knowledge contained within the text is
more visible than any others.

As opposed to pure text, we can look at a spreadsheet where the boxes
are called “evidence”, “counter-evidence”, “hypothesis”, etc. Here,
Suthers found that students were very eager to fill out all the empty
boxes, which then “forced” them to think about what their theory was,
what the consequences of that theory would be, etc — thinking in a much
more systematic fashion than they would, had they used pure text to
collaborate.

He also experimented with various forms of mind maps, concept maps, etc.
If you force students to name the link, every time they link two nodes,
you force them to think about and be aware of why they link two concepts
together. By doing these experiments with a large number of students,
and both videotaping the actual process, but also analyzing the final
artefact that the students produce, rich data about how systems with
various salient features led students to think in different ways.

h3. Shared artefact

One important function that the external representation plays in a group
setting, is as a shared referent for discussions. It is useful to
distinguish between artifact and discourse, as two overlapping but
different concepts. In a face-to-face meeting, this distinction is very
clear — an artefact is whatever is captured, and shared between the
participants, as opposed to the fleeting conversations that in most
cases is not recorded at all. This means that the artefact not only
maintains the participants’ “current state of knowledge” during the
workshop, but also gets to represent the outcomes, and the collective
memory of the event.

In an online environment, where everything is captured, the distinction
becomes more blurry. However, in most cases, you can distinguish between
discourse as more cumulative, and artefact as more integrative. A
threaded discussion forum is a good example of a discourse setting, with
each post contextually bound to a specific context (chronology, and the
post that it replies to), the conversation moves inexorably forwards,
and it is not that interesting to back, unless you want to trace how
ideas developed.

An integrative artefact is more like a wikipage, which develops and
changes to always provide an up-to-date representation of the
community’s current state of knowledge. This example is captured in
Wikipedia, where an article is the integrative artefact, and the
Talk-page, which features discussions about the article, is the
discourse.

Suthers and his team has have experimented with building collaborative
learning platforms that integrate artefact-centric discourse (Dwyer and
Suthers 2005; Lid and Suthers 2003; Suthers and Xu 2002). In addition to
making sure that both the artefact and the discourse are visible at the
same time, they have explored the concept of deixis, how you can point
or refer to a specific point on the artefact (especially for a graphical
artefact). This is what students in Knowledge Building classrooms do,
when they meet for “Knowledge Talk” (Bereiter 2004). They
have a big representation up on the screen, and they point to it. -
“What about //this// note, should it be connected to //this other//
note?”. But in an online setting, how can you effectively convey to
other students which one you mean by “//this//”?

An example of attempting to overcome this problem is an environment
developed at Drexel University called Math Forum (Stahl 2006). The tool
provides a shared whiteboard where people work together on solving math
problems, and they have a text chat on the right. For each message, they
can point to something on the screen, and there is a line that connects
your text message to the area of the screen that you are interested in.
If you go back in time, and click on that message, and the message says
“I think we should delete this”, you would immediately know what that
person meant. They also have something similar where you can work on a
document together, select a few paragraphs and say “I think we should
delete this”, and others can go back in the chat, and see exactly what
you were referring to.

h2. Manipulating ideas

In this section, I will introduce two different models for how the
development of ideas can happen in online courses. Then I will look at
applying the first model, divergence and convergence, to the workshop
and individually working with ideas settings.

h3. Stimulus/response

Stimulus/response can describe a very typical way of organizing an
online course, where you have a forum each week, and you start with the
teacher providing some stimulus — it might be a reading, or a recording,
or some new ideas that the teacher introduces in this space. The
students know that they have to participate, because of the
participation marks, and because people want to look good in front of
the teacher, so they will contribute notes — any notes. Personal
anecdotes, free association, whatever they can come up with that has
anything at all to do with this topic.

In a typical OISE course, many will have been teachers, so there might
be many comments like “I remember when this happened in my class”, etc.
There might be some scattered discussion or response, when people have
new associations based on what their classmates posted, but it quickly
dies down when people have “said what they had to say” — until some new
stimulus is introduced.

At the end of the course, what have the students gotten out of it? They
have been exposed to some ideas, and they have heard some of their
co-students' ideas. But this does not seem very ambitious for a graduate
level course. Likewise, Bereiter (2004, 254), discussing a
group project where students gather information about polar bears, ask:

<blockquote Bereiter (2004, 254)>But what do they learn about polar bears from producing a multimedia
document on polar bears? It all depends on what information they
process in assembling the document. If the only questions they
consider are “Is it about polar bears?” and “Does it look nice?” we
may infer that not much polar bear knowledge will be acquired.</blockquote>

h3. Divergence/convergence

Another way of organizing a course can be described as a cycle of
divergence and convergence. When diverging, we are brainstorming, freely
associating, coming up with as many new ideas as possible, without being
too critical about their utility or relevance. This is similar to the
first five weeks in the course taught by Scardamalia, where students
where posting their responses to the initial “stimuli”; or the initial
phase of the workshop, where people are creating sticky notes with ideas
for topics to discuss.

However, a brainstorming that ends without doing anything with the
produced material is not very valuable, and this is essentially another
way of characterizing the first model (stimulus/response). What makes
the second model different, and more valuable, is that after the
participants have generated a large amount of material, they stop, take
a step back, and begin to analyze and look at what they have created.
What are the emergent topics, groupings, connections that are forming?
In many ways, this is similar to a grounded theory approach to
qualitative research, when researchers pour over interview data to look
at emerging topics (see for example Kelle 1997).

h3. Improvable ideas

One of the core ideas of Knowledge Building relates to the improvability
of ideas, where “Participants work continuously to improve the quality,
coherence, and utility of ideas” (Scardamalia 2002). One of the things I
found so powerful about Knowledge Forum, with it’s spatial display of
notes, is that notes (and by extension, ideas) are not caught in the
location where they were first posted, rather they can easily be moved,
and also copied, to other view — an example of this is the topical views
that we created in Scardamalia’s class.

This is similar to the affordances of sticky notes at workshops, they
can be moved around on the wall, and are very accessible to everyone
(not just one person controlling a computer) (Gunn 2008; Peterson and
Barron 2007).

Although a spatial interface is very suited to this kind of knowledge
work, we can imagine other interfaces also implementing similar
functionality. For example, Knowledge eCommons is an application
developed at OISE, which is purely textual. An experiment that has
already been implemented is a split-screen view with a collaborative
editing board (Etherpad) on the right, and the notes on the left. This
is a classical integration between discourse and artefact, which was
mentioned above (although currently there is no method for referring to
a specific part of the artefact, in the discourse).

One possibility for letting notes be more “moveable”, would be to use
tagging. Tagging can be used in many ways — for example as folksonomies,
where people tag things as they produce them, without knowing which tags
others have agreed upon (Sinclair and Cardew-Hall 2008). This works
great in large communities like Twitter, but would probably not be
meaningful in a small study group.

Another approach is to hold off on tagging until you have established
certain categories, and then use tags essentially as a form of “global
categories” or channels. An example is that when I tag my tweet with
#ocwc2011, I know that it will end up in the channel for people who are
interested in the OpenCourseWare Consortium meeting (Hepp 2010). In the
same way, students could go back to their previous readings after a few
weeks, find a few emergent topics, tag messages related to this topic,
and then have the system automatically create a new view which would
contain all the tagged messages, and a blank collaborative editing board
for knowledge building about this topical view.

h3. Micro- and macro-levels of collaboration

Many would object to my description of collaborative software, and their
ability to enable or not deep knowledge building, as well as the tieing
of thinking patterns (divergence/convergence and stimuli/response) to
certain technological structures, or physical workshop tools. As Downes
(2011) posited in his defence of the traditional lecture, it is quite
possible for deep engagement to be happening in the mind of someone
sitting in a large lecture hall, receiving a lecture.

And as we have discussed tools and approaches for individually working
with ideas, such as Tony Buzan’s mind maps (Buzan and Buzan 2006), a
committed student could certainly participate in a traditional
discussion-forum based online course, taking notes, creating concept
maps, synthesizing the ideas, and then posting a new entry that brings
together many threads and ideas from different parts of the discussion
forum.

To tackle this question, I would like to introduce the idea of different
granularities of collaboration — how much of the working with ideas
takes place in your own head, and at what point do you share your
thoughts with others? On one end of the spectrum, at a micro-level of
collaboration, you might have two people who knew each other well, who
were engaged in a discussion about a problem, vocalizing every idea that
comes through their mind, working together as one mind to solve the
problem.

At the very other far end of the scala, the macro-level of
collaboration, might be found the scientific world, where individual PhD
researchers might read hundreds of books and articles, take thousands of
pages of notes, diagrams, before they finally publish their
dissertation, which deals with decades worth of literature. A few years
after the publication of the dissertation, an article might be published
that builds upon the ideas in the dissertation, a few years later, a new
article, and so on. (Of course, the PhD student might be part of a local
research team, and might exchange ideas with them at a much lower
granularity).

This is a useful distinction, because it covers many areas where we
instinctively feel that there is collaboration and knowledge building
going on, such as the edublogosphere, where you can see a progression of
ideas and a community understanding of the current state of knowledge in
the field, yet there are no affordances in the tools we use, that make
this knowledge building easier.

We can also experiment with lowering the granularity of collaboration in
fields where the granularity to the public has traditionally been quite
high - an example would be the movement for Open Notebook Science in
academia, where researchers who would traditionally hoard their lab
results until the final paper was published are not voluntarily sharing
their data in almost real-time with anyone who might be interested
(Bradley 2007).

I began this paper by positing that there were commonalities in the way
we work with ideas individually, the way groups work with ideas in
hybrid or online spaces, and in the way innovative approaches to
workshops try to improve the knowledge building that happens with people
collaborating in physical spaces.

I introduced three examples of these different settings, and discussed a
number of analytics concepts, taken both from the literature, and from
my own experience. I mentioned the importance of externalization of
ideas, which both relates to spatial organization and salience of the
medium for expressing the ideas, as well as the importance of knowing
that the ideas are “safe”, which frees up energy to focus on a subset of
the ideas (by “yanking” them to the centre).

I then introduced two different idealized models of collaboration
scripts in online classes, based on two examples that I personally
experienced, discussed software support for improvable and movable
ideas, and introduced the concept of granularity of collaboration to
explain why knowledge building might also occur in environments that do
not seem to support the concept of improvable ideas natively.

I believe this paper has demonstrated that there is fruitful terrain in
exploring the intersections between individually working with ideas,
group collaborative learning, and workshop methodologies (about which,
unfortunately, there seems to be very little academic literature). It
would be interesting to hear people active in facilitating collaborative
courses reflect upon how they as individuals process information, and
also on how workshops and conferences organized for people within
Computer-Supported Collaborative Learning could better foster
collaborative knowledge building.

h1. References

Allen, D. 2001. *Getting things done: The art of stress-free
productivity*. Penguin Group USA.

Bereiter, C. 2004. Education and mind in the
knowledge age. *British Journal of Educational Psychology* 74: 127–38.

Bernstein, M. 2007. *The Tinderbox Way*. Eastgate Systems, Inc., USA.

Blair, Ann. 2010. *Too Much to Know: Managing Scholarly Information
before the Modern Age*. Yale University Press.

Bradley, J. C. 2007. Open notebook science using blogs and wikis.

Buzan, T., and B. Buzan. 2006. *The mind map book*. Pearson Education.

Downes, Stephen. 2011. The Lecture Must Stand.

Dwyer, Nathan, and Daniel D. Suthers. 2005. A study of the foundations
of artifact-mediated collaboration. *Proceedings of the 2005 conference
on Computer support for collaborative learning learning 2005: the next
10 years! - CSCL ’05*: 135–44. doi:10.3115/1149293.1149311.
http://portal.acm.org/citation.cfm?doid=1149293.1149311.

Gunn, Allen. 2008. *Creating Participatory Events*.

Hepp, M. 2010. HyperTwitter: collaborative knowledge engineering via
twitter messages. *Knowledge Engineering and Management by the Masses*:
451–61.

Kelle, U. 1997. Theory building in qualitative research and computer
programs for the management of textual data.

Lid, Viil, and Daniel D. Suthers. 2003. Supporting Online Learning with
an Artifact-Centered Cross-Threaded Discussion Tool. *Computers in
Education*.

Peterson, Elizabeth R., and Kevin A. Barron. 2007. How to Get Focus
Groups Talking: New Ideas That Will Stick. *International Journal of
Qualitative Methods* 6.

Scardamalia, Marlene. 2002. Collective cognitive responsibility for the
advancement of knowledge. *Liberal education in a knowledge society*:
67–98.

---. 2003. Knowledge Forum (advances beyond CSILE). *Journal of Distance
Education* 17: 23–28.

Sinclair, J., and M. Cardew-Hall. 2008. The folksonomy tag cloud: when
is it useful?. *Journal of Information Science* 34: 15.

Stahl, G. 2006. *Group Cognition: Computer Support for Collaborative
Knowledge Building*. Cambridge, MA: MIT Press.

Suthers, D. D. 2008. Empirical studies of the value of conceptually
explicit notations in collaborative learning. In *Knowledge Cartography:
Software tools and mapping techniques*, ed. A. Okada, S. Buckingham
Shum, and T. Sherborne, 1–23. Springer.

Suthers, Daniel D. 2001. Towards a Systematic Study of Representational
Guidance for Collaborative Learning Discourse. *Journal Of Universal
Computer Science* 7: 1–24.

Suthers, Daniel, and Jun Xu. 2002. Kükäkükä : An Online Environment for
Artifact-Centered Discourse. *World Wide Web Internet And Web
Information Systems*: 472–80.