The Techno Files: John Bordeaux
The Techno Files is a series of short interviews featuring people associated with the MA in Learning and Technology (MALAT) program. John Bordeaux is on the MALAT thesis supervisor list.
Why are you interested in learning and technology?
JB: After spending a year and a half working with an education foundation, I had the opportunity to learn about the alignment among technology, pedagogy, and cognitive science. I was also privileged to work alongside researchers from educational research organizations, and learned that affective skills matter as much if not more than cognitive ones for student success. No amount of information technology nor curriculum can replace the student’s emotional engagement in successful learning. Technology, properly applied, can activate engagement. Just one example: young people are motivated towards online video games - by using these methods and technologies to help the student navigate information, we can help advance learning by appealing to a natural interest in challenging games.
The other area that sparks my interest is the use of social technologies to help the stakeholders in a child’s life - coach, family, teachers, siblings - to become engaged and aware of the students struggles and progress. By lowering the transaction costs for the student’s ‘network’ to remain aware; we may increase the opportunities for intervention and conversation.
You are known for your expertise in knowledge management. I find people often have narrow or conflicting ideas about MALAT-related fields such as organizational learning, formal education, or organization development. How do you think about the relationships between knowledge management, learning and technology?
JB: I wrote a white paper on this topic in 2012, excerpts included as part of this answer.
Organizations, like people, live in a continuously changing world. Interactions among disparate elements create futures far removed from those planned - this can be observed on both a micro and macro scale. For the organization, agility in the face of change and surprise is crucial to survival and success. If we imagine an entity in which individuals consistently achieve their goals — no matter the challenges at hand — and where success breeds expanding capability, not only on an individual basis, but also enterprise-wide then we are envisioning an organization that has achieved status as a “learning organization.” Learning organizations are able to adapt to change and move forward successfully by acquiring new knowledge, skills, or behaviors, thereby transforming itself. (Allee, Verna. (2003)). The Future of Knowledge: Increasing Prosperity Through Value Networks. Amsterdam: Elsevier). Learning in organizations means the continuous testing of experience, and the transformation of that experience into knowledge – accessible to the whole organization, and relevant to its core purpose. (Senge, P. et. al. (1994) The Fifth Discipline Fieldbook: Strategies and Tools for Building a Learning Organization)
But what does it mean to be accessible? Consider Karl Weick’s observations regarding decision making in a changing (that is, our only) world:
“[I]t is not just the old answers that are suspect. It is also the old questions. And once people are uncertain what questions to ask, then they are put in the position where they have to negotiate some understanding of what they face and what a solution would look like. Puzzles now represent both threats and opportunities, the same event means different things to different people, and more information will not help them. What will help them is a setting where they can argue, using rich data pulled from a variety of media, to construct fresh frameworks of action-outcome linkages that include their multiple interpretations. The variety of data needed to pull off this difficult task are most available in variants of the face to face meeting.” (Weick, K. E. (1995). Sensemaking in Organizations. Thousand Oaks, CA: Sage Publications.)
Early efforts to define and enable learning organizations led to the doomed concept that experience could be captured and made explicit - removing the need for apprentice training or mentorship; modes that are difficult to scale across the enterprise. The marriage of KM and Learning Organization therefore birthed hundreds of failed initiatives to ‘capture’ experiential knowledge, and “make it available” in an online setting. The belief held that the learner would simply review the information that had been captured, and would thereafter be as proficient as the individual from whom the experiential knowledge had been captured. Finally, online tools would enable an organization to scale the management of knowledge in ways that mentoring and apprentice networks could not.
This is often referred to as the early “generation” of KM, where the writings and tools of the time left the clear impression that knowledge resides in documents. Associated ideas included the notion that ‘tacit’ knowledge could be somehow ‘made explicit.’ Since then, with a few years’ experience that demonstrated the failure of this approach, the KM discipline evolved to embrace the radical idea - borne out by cognitive science - that individual knowledge is socially constructed.
Tacit knowledge, as defined by Polanyi, (Polanyi, M. (1966). The Tacit Dimension. Garden City, New York: Doubleday) is that which cannot be articulated, that which we know but do not know how we know it. Canonical cases include riding a bicycle or an effective golf swing: one does not acquire these skills through reading or PowerPoint alone. Certain unconscious mechanisms must be engaged to ‘become a golfer’ or ‘become a rider.’ While procedural and process information may be captured and made available, and compliance or transactional information may be made explicit - there is a host of what it means to become a professional in a given discipline, such as HR, that cannot be learned without experience. In addition, we are finding that such experiential learning also requires other people. That is, all learning is social. “Social learning is what it sounds like - learning with and from others. It has been around for a long time and naturally occurs at conferences, in groups and among old friends in a cafe.” (Bingham, T., & Conner, M. (2010). The New Social Learning: A Guide to Transforming Organizations Through Social Media. San Francisco, CA: Berret-Koehler Publishers, Inc.)
After some years in the document-centric wilderness, the KM discipline began to recognize the social nature of knowledge exchange (learning), and embraced methods such as Social Network Analysis. Tools began to emphasize the ability of people to find ‘experts,’ or at least other people facing similar problems. Scaling Weick’s variants for the ‘face to face meeting’ became more central to the KM mission, which aligned it more with the Learning Organization goal.
To learn is to optimize the quality of one’s networks - Jay Cross (Cross, J. (2010). Working Smarter: Informal Learning in the Cloud. Berkely, CA: Internet Time Group.)
Human knowing is fundamentally a social act - Etienne Wenger (Wenger, E. (1999). Communities of Practice: Learning, Meaning, and Identity. Cambridge, UK: Cambridge University Press.)
Our realty is shaped by our social interactions. These interactions provide context - socially scaffolding what you have learned with what another person has learned and so on. This generates a virtuous spiral, socially generated and built and more powerful than any one participant could create individually - Bingham & Conner. (Bingham, T., & Conner, M. (2010). The New Social Learning: A Guide to Transforming Organizations Through Social Media. San Francisco, CA: Berret-Koehler Publishers, Inc.)
If individual knowledge is socially constructed, than a focus on setting the conditions for social learning become a core goal for KM initiatives. But how does this translate to organizational performance and better decision making, the goals for the Learning Organization? One answer is provided by James Katzenbach in his seminal work, The Wisdom of Teams: “The truly committed team is the most productive performance unit management has at its disposal - provided there are specific results for which the team is collectively responsible, and provided the performance ethic of the company demands those results.” (Katzenbach, J. R., & Smith, D. K. (2006). The Wisdom of Teams: Creating the High-Performance Organization. New York, NY: HarperCollins Publishers.)
KM’s contribution to the learning organization, then, lies in its ability to not only connect people to the information they need to excel at their jobs, but to help them develop experiential knowledge through social networks that scale, and to set the conditions (including communities of practice) for them to participate in team structures for shared work. Using Katzenbach’s terms, part of the reason for a Community of Practice is to allow teams to emerge from groups: “Groups become teams through disciplined action. They shape a common purpose, agree on performance goals, define a common working approach, develop high levels of complementary skills, and hold themselves accountable for results. And, as with any effective discipline, they never stop doing these things.” (Katzenbach, J. R., & Smith, D. K. (2006). The Wisdom of Teams: Creating the High-Performance Organization. New York, NY: HarperCollins Publishers.)
Put simply, KM connects people to information but also one another for shared value.