Update on Project Natal
In my October 21, 2010 blog post on situated learning spaces, I mentioned Project Natal’s virtual boy named Milo (there’s also a virtual girl named Kate), and posted a 2009 demo launch of Milo. Peter Molyneux, head of Microsoft’s European games division, has since appeared on TEDGlobal to talk about Project Natal (July 2010), where he showed the real Milo technology on TED’s stage:
We see the demonstrator interacting with Milo using body gestures, facial gestures, and voice – all of which needs to be “taught” to Milo.
Milo’s Mind as Metaphor of Knowledge Building
What really interests me about Milo is what Molyneux says in his TED presentation about Milo’s mind: “Milo’s mind is in a cloud”. In other words, the more people who use Milo, the more objects he’ll learn and recognize. Fascinating! To me, this implies that Milo’s artificial intelligence is distributed, and Milo’s mind is the synthesis of training combinations contributed by a community of Milo users. Milo’s mind is a wonderful metaphor for collaborative knowledge construction, where Milo’s mind represents a knowledge community’s World 3 Rise Above knowledge artifact, and the community of Milo users is the knowledge community.
Situating Milo’s Distributed Mind
In my October 21, 2010 blog post, I had hypothesized that “the more the situated learning context approximates a realistic context of the domain of study, the deeper and more meaningful the learning will be”, implying that Milo could contribute to deeper situatedness in wider contexts.
Since then, I have re-read Carl Bereiter’s 1997 book chapter, Situated Cognition and How to Overcome It. He mentions what he and Scardamalia (1989) have termed intentional learning, which has 3 goal levels:
Task completion goals – to be completed immediately (e.g., assignment due date tomorrow); World 1; highly situated
Instructional goals – to be completed summatively in the near future (e.g., end of the course); world 2; less situated; somewhat transferrable
Knowledge building goals – may extend indefinitely into the far future and past; World 3; weakly situated (i.e. immediate situation); highly transferrable
In my December 7, 2010 blog post, I mentioned Bereiter’s reasoning behind why it would be prudent to overcome situated cognition – situated learning is inversely proportional to generalizability of that knowledge. Thus, an individual’s knowledge would be high transferrable if that knowledge was learned with an intentional orientation toward knowledge building goals.
Continuing on this train of thought, when I consider the “learning” that Milo “does” as he is being used by multiple members of the Milo user community, I would say that almost all of these learnings are for task completion goals. Thus Milo’s artificial intelligence should be highly situated and therefore highly ungeneralizable. If we assume that Milo will have a large and active globally-distributed user community, this alone would exponentially increase Milo’s potential learning situations/contexts. If Milo is taught by this large globally-distributed user community to recognize an object – a chair for example – in multiple and varied situations, then he should be able to recognize chairs of all types and across all situations. In other words, the object “chair” mimics generalizability. Have I found a way to achieve a mimicry World 3 with a low-level goal of task completion – by exponentially increasing learning situations contributed by a large knowledge community?
This brings to mind the authors of Wikinomics (2008) and Macrowikinomics (2010) Don Tapscott and Anthony Williams’ idea of Murmuration Macrowikinomics. Here’s a narrated video metaphor of it:
Tapscott is careful to note that “…this is not a collective intelligence or collective consciousness of course, because individual birds are not intelligent or conscious…”. He describes the thousands of starlings moving as one mass, as a “…loosely conjoined network of relationships and impulses” – much like a globally-distributed Milo user community?
Tapscott asks: “Will we come to consider networking as the neural roots that connect human beings in a way that creates something fundamentally new?” I do not hesitate to say “YES – but…..!” Much like Milo’s “mind” (i.e. artificial intelligence) cloud, that grows as his distributed user community “teaches” him more, Milo’s knowledge base will only ever mimic World 3, and appear to be generalizable. What allows Milo’s mind to mimic generalizable knowledge is the numerous situations in which he has learned the same thing. If millions of users around the world “teach” Milo to recognize the object known as “chair”, presumably, they’re all using different kinds of chairs available in their homes. These chairs could vary greatly in age, design, style, size, etc. Hence, Milo acquires the ability to recognize any chairs that match any one of the millions of chairs his distributed user community has inputed into his memory stores – giving the appearance that Milo has generalized recognition of the object called “chair”. Milo’s seeming intelligence is indeed artificial.
Back to Tapscott’s question. I think that networking neural roots connecting people would create something fundamentally new, but not necessarily better. Like Milo’s mind cloud, this new thing would only mimic World 3 due to the exorbitant number of task-completion-oriented contributions to its knowledge base from its massive and globally-distributed user community. Highly transferrable World 3 knowledge can only be created with an orientation toward knowledge building goals, and must consider the far past and continue into the far future (Bereiter, 1997).
References
Bereiter, C. (1997). Situated cognition and how to overcome it. In D. Kirshner & J. A. Whitson (Eds.), Situated cognition: Social, semiotic, and psychological perspectives (pp. 281-300). Hillsdale, NJ: Erlbaum. Available: http://www.ikit.org/fulltext/1997situated.pdf
What is Knowledge?
Knowledge is socially negotiated, situated, and distributed. It is what Sir Karl Popper would term a “World 3” artifact, it’s own entity, taking on a form of its own (what Scardamalia and Bereiter would call a “conceptual artifact”) within a knowledge community.
Knowledge is situated in the sense that concepts are situated within the context of the surrounding environment, and these concepts are progressively developed. It is a product of the activity, context and culture in which it is learned and used. Conceptual knowledge as a tool is only fully understood through its use. Furthermore, its use changes the user’s world view, causing the user to adopt the belief system of the culture in which the conceptual knowledge tool is used (Brown, Collins, and Duguid; 1989). Brown, et al. (1989) propose a “cognitive apprenticeship” model of education, which promotes learning through activity, tool, and culture. Cognitive apprenticeship is supported by collaborative social interaction and social construction of knowledge. It honours the situated nature of knowledge, embeds learning in activity, and makes deliberate use of the social and physical context.
Bereiter (1997) argues for the value of overcoming situatedness of cognitionby creating World 3 knowledge objects that are transferrable across different situations. He points out that the problem with situatedness of cognition is that as one learns more about a phenomenon, one knows increasingly more about the phenomenon as framed within that situation, making this knowledge less likely to be generalizable to other situational contexts. Furthermore, treating all knowledge as situated would make knowledge objects invisible. Bereiter (1997) calls us to treat ideas as “objects of inquiry” (p. 18), requiring “Disciplined movement back and forth between (Popper’s) World 1 (the physical world) and World 3 (immaterial world of knowledge and abstract objects)” (p. 18), yielding the “hypothetico-deductive method” and endless possibility for theory development, problem solving, and design. Incidentally, Sir Karl Popper’s philosophy of knowledge is based on his proposition of 3 worlds:
World 1 – the physical world
World 2 – the world of the mind (mental states, ideas, perceptions)
World 3 – the immaterial world that is the product of the human mind (e.g. the concepts and ideas represented or expressed by books, papers, paintings, symphonies; but not physical books, papers, or paintings themselves)
What is Mind? Where Does Knowledge Reside? Sebastian Seung of MIT discusses his theory of mind: “I Am My Connectome” at a TED Talk (July, 2010):
Nueroscientists since the 19th century have hypothesized that one’s memories, personality, and/or intellect also reside in the connections between one’s neurons. This would imply that knowledge resides in our neural connections. These connections form what Seung calls one’s “connectome“. According to Seung, neural activity encodes our thoughts, feelings, and mental perceptions; but neural activity can also cause our own neural connections to change (hence changing our connectome). Conversely, our experiences can change our connectome. Thinking can change one’s connectome. Neural activity constantly changes in our brain, but it is the brain’s neural connections (which make up the brain’s neural network) that determine the pathways along which this neural activity flows. These neural pathways as a whole is the connectome. The Seung Lab at MIT seeks to read memories from connectomes, and Seung is leading the new field of connectomics to test these hypotheses.
As I consider both Bereiter and Seung’s theories of mind, I see the one commonality is that they both see knowledge as residing in connections and composed of networks of connections. In Seung’s case, these connections are of a physiological nature residing in the human brain. In Bereiter’s case, these connections are conceptual in nature, residing in World 2 and World 3; with collaborative knowledge building as the vehicle by which individual learner’s conceptual connections combine and build upon those of other learners in the creation of a World 3 knowledge object.
The Nature of Knowledge: Implications on Assessment
Bereiter (1997) points out that situativity theorists have not advanced any educational ideas due in part to their failure to define the purpose of education and to their confusion between process and product. The collaborative knowledge building that I have witnessed in my experience as a graduate student, classroom teacher, and instructional leader aligns with Bereiter’s assertion that “knowledge implicit in the process” (p. 23) should be distinguished from “knowledge that is the product of the process” (p. 23); and yet, this distinction is not recognized in K-12 classroom practice, curriculum policy, or assessment and evaluation policy. Our education system is still misguidedly preoccupied with learning goals and the embodiment of knowledge in the form of reports and presentations, failing to see knowledge taking a form that can be worked with or even packaged and sold (Bereiter, 1997). This is decidedly antiquated thinking, considering our knowledge society’s wealth will come from knowledge work – no longer from manufacturing as in the industrial society of old. “What distinguishes knowledge work is not using knowledge by creating or adding value to it” (Bereiter, 1997: p. 23). This is not to say that we should do away with learning goals, merely that the focus should shift from these to learning for deeper understanding and the construction of knowledge through collaborative means supported by technology.
Hutchins and his colleagues developed the distributed cognition approach in the 1980s as a way of understanding collaborative work practices, or “cognitive systems” – complex interactions between multiple people, artifacts to perform an activity, and technological systems (Rogers, 2006). There are two levels of analysis: (1) analysis of ‘the propagation of representational state across media’ and (2) analysis of human interactions (i.e. the problems, the breakdowns, distributed problem-solving, role of non/verbal behaviour, coordinating mechanisms, communication during collaboration, knowledge sharing/accessibility).
While I can see the value of having a distributed cognition framework with which to study the dynamics of collaborative work, I find that this approach emphasizes the interactions enacted to complete the task, and does little to examine closely how ideas/memes/theories evolve within and among a community. Furthermore, the distributed cognition approach does not seem recognize knowledge as its own entity, as a ‘product’. Rather, it acknowledges knowledge as a process but focuses on the knowledge community’s development of that process, not on the conceptual evolutionary process of knowledge in its own right.
This is especially problematic when one considers Carl Bereiter’s (2002) contentions that that the mind is not a container, knowledge and conceptual artifacts do not reside in learners’ minds, and that the mind does not reside in the head of a person, nor does it necessarily reside within one person. For Bereiter, knowledge objects and conceptual artifacts belong to World 3.
If distributed cognition analysis of a knowledge community can be used in such a way as to reveal how the community’s mind/cognition is distributed among the community, and hence, trace how the conceptual development of knowledge as a conceptual artifact is gradually constructed within the community; then distributed cognition could be a powerful approach to prove Bereiter’s theory of mind, and aid in the classroom assessment of collaborative knowledge building in asynchronous online environments.
In this regard, Activity Theory (Hew & Cheung, 2003), developed by Alexei N. Leont’ev and Sergei Rubinshtien, seems more promising. Based on the premise that tools mediate between subject and object processes, rules mediate between subject and community processes, and roles mediate between community and object processes; one can use this framework to analyze several combinations of any three processes as illustrated by any three edges of the following triangles:
If the basis of our knowledge society’s economy is going to be knowledge work, then it stands to reason that schools must teach learners to be proficient knowledge workers. Assessment is vital to this learning process, for teachers and learners alike. Applying Gunawardena et al’s (1997) 5 phases of active knowledge construction and Henri’s (1992) 5 types of critical thinking (Hew & Cheung, 2003) to assess individual and community knowledge construction would be very useful in this regard. In addition to being good knowledge workers, today’s learners will need to become excellent collaborators as well. With this in mind, it would be helpful to apply Rourke et al’s (1999) 3 constructs of social presence and affective responses (Hew & Cheung, 2003) to assess social presence, and to apply Kirkley et al’s (1998) 7 means of moderator assistance (Hew & Cheung, 2003) to assess facilitation skills – or what I like to call “cognitive leadership” skills. Of course, I do not mean to suggest that we implement the above mentioned frameworks in their pure form, but rather to reform our current assessment and evaluation policies and practices based upon a combination of these frameworks.
Bereiter, C. (1997). Situated cognition and how to overcome it. In D. Kirshner & J. A. Whitson (Eds.), Situated cognition: Social, semiotic, and psychological perspectives (pp. 281-300). Hillsdale, NJ: Erlbaum. Available: http://www.ikit.org/fulltext/1997situated.pdf
Brown, A. L., & Campione, J. C. (1996). Communities of Learning and Thinking, or a Context By Any Other Name. Contemporary Issues in Teaching and Learning, 120-126.
Hew, K. F. & Cheung, W. S. (2003). Models to evaluate online learning communities of asynchronous discussion forums. Australian Journal of Educational Technology 2003, 19(2), 241-259. Online: http://www.ascilite.org.au/ajet/ajet19/hew.html
Scardamalia, M. (2002). Collective Cognitive Responsibility for the Advancement of Knowledge. Liberal Education in a Knowledge Society, 67-98.
Scardamalia, M. (2000). Social and Technological Innovations for a Knowledge Society. In S. S-C. Young, J. Greer, H. Maurer, & Y.S. Chee (Eds.). Proceedings of the ICCE/ICCAI 2000: Volume 1. Learning Societies in the New Millennium: Creativity, Caring & Commitments. (pp. 22-27). National Tsing Hua University, Taiwan: Taipei. Online: https://portal.utoronto.ca/bbcswebdav/xid-1113975_1
Scardamalia, M., & Bereiter, C. (1996). Student Communities for the Advancement of Knowledge. Communications of the ACM, 39(4), 36-37.
I’ve been learning a lot about knowledge communities – including Reciprocal Teaching – (Ann Brown, Anne Marie Palincsar), collaborative knowledge construction (Marlene Scardamalia, Carl Bereiter), and distributed learning (Yvonne Rogers, Michael Cole & Yrjo Engestrom). However, when I think of “the tubes” (i.e. YouTube, SchoolTube, TeacherTube) and all the learning opportunities they have to offer via traditional transmission instructional strategies – the very strategies we are desperately trying to steer away from – I find myself at a crossroads. The examples of YouTube learning below have absolutely ZERO aspects of community knowledge creation other than YouTube’s role as video tutorial repository. Granted, YouTube does allow for the posting of comments for each video, but I have yet to see this feature being used in a way that remotely resembles a community discourse with the goal of deeper understanding. What follows are some examples of the learning power of YouTube.
Khan Academy
Not sure if anyone has heard of the Khan Academy? Salman Khan, who quit his job in finance, runs this non-profit educational organization out of a closet in his home. He regularly creates tutorial videos about an astounding number of science and math topics, and posts them to YouTube. Here’s a chemistry video about the atom, proton, neutron, and electron:
Common Craft
Then there’s Common Craft, who makes wonderful videos to explain various technologies (e.g. wikis, blogs, augmented reality, etc.). Here’s Common Craft’s YouTube channel. Their latest video is about augmented reality:
My 8-Year-Old Nephew
Just to illustrate my classmate, Maria’s, point about the power and appeal of YouTube, I’d like to use my 8-year-old nephew as an example. Aside from a few ukelele classes he got at his after school program, he has taught himself how to play the ukelele and the guitar by surfing YouTube. YouTube has fed his motivation to learn both these instruments so much, that he now creates his own ukelele songs (sometimes with accompanying lyrics). He also gets his parents to take him to local open mic events at least once a week so that he can perform his music. Furthermore, he gets his mom to video-record his performances so that they can be uploaded to YouTube. Makes me wonder what he’ll do at age 10. Here’s my nephew’s YouTube channel if you’re interested. My favourite song is “I Don’t Know” – hysterical!!!
YouTube for Knowledge Communities & Distributed Cognition?
Contrary to everything I’m learning in graduate school about learning, YouTube, for the most part delivers knowledge in the most pedagogically traditional way. There is no knowledge community discourse, no distributed cognition, no acknowledgement/accommodation of individual learning styles…it is simply transmission teaching in a digital medium! Yet, it’s undeniable that such instructional YouTube videos have learning value. So how might we better use YouTube to enhance learning, promote distributed cognition, and foster knowledge communities?
My first thought is to make better use of the YouTube’s commenting functionality, and use this as a community discussion forum. However, I think it is better to establish an online learning environment on some other platform, and refer to specific YouTube video resources as the need arises. This way, the instructor has more tools to work with, in terms of creating an effective online learning environment. Furthermore, the instructor and participants (through their virtual social presence) may have a clearer picture of how the community’s cognition is distributed and therefore take reasonable actions in their responses to focus on topics/directions that suit the group’s needs.
Case Study Approach
A more concrete strategy to ensure distributed cognition and distributed problem solving within a knowledge community would be to design a specific and authentic problem to be solved by the group as a whole. Some of my classmates have suggested the case-study method to achieve this.
Knowledge Community & Inquiry (KCI)
Another framework that comes to mind is Knowledge Community and Inquiry (KCI). The theoretical tradition of classrooms as knowledge communities (Brown & Campione, 1996; Scardamaila & Bereiter, 1999; Hakkarainen, 2004), engages students with a sense of “collective cognitive responsibility” for continuous community knowledge advancement (Scardamalia, 2002). KCI is a recent effort within this tradition espoused by James D. Slotta and his colleagues (e.g., Slotta & Peters, 2008; Peters & Slotta, 2010). KCI engages the students and teacher as a knowledge community, building a cooperative “knowledge base” that serves as a resource in scaffolded inquiry activities designed to target specific learning goals. I believe KCI can be a powerful approach to focusing a knowledge community’s distributed cognition on the collaborative inquiry and problem solving.
