Paper written for a course at OISE. Based on presentation to CCK11 (extended notes).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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:

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.

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).

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.

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.

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