avron-barr

Meet Avron Barr

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CIRCL perspectives offer a window into the different worlds of various stakeholders in the cyberlearning community — what drives their work, what they need to be successful, and what they think the community should be doing. Share your perspective.

Avron Barr

Avron Barr is a principal of Aldo Ventures, Inc.

How did you get started in cyberlearning?

I got started in Cyberlearning in 1972. I had dropped out of a PhD program in Physics at Berkeley, and I got a job as a programmer at Stanford’s Institute for Mathematical Studies in the Social Sciences. The Institute’s Director was, and still is, Patrick Suppes, a brilliant Renaissance man who led teams doing pioneering work in several disciplines. One of those research areas was Computer-Assisted Instruction, as we called it back then.

One of my jobs was to maintain and add to the code for a “drill and practice” system that taught arithmetic and language skills to 3rd graders in a nearby school. At that time, there were no PCs and no networks. The children went into a trailer once a day for 20 minutes where we’d set up Model 33 Teletype machines which were connected by telephone to the Stanford computer. The kids wore headsets which were also connected to Stanford by telephone lines. The drill and practice programs included audio instructions and feedback. Another of my responsibilities was to record the audio — it was a primitive digital audio system and my voice worked the best for recording.

Here’s what I saw that sold me on Cyberlearning: these kids were in East Palo Alto, which at that time was an economically challenged community. I interacted with them in the trailer almost every day. Basically, they were debugging my software, since I wasn’t a very good programmer. They would run up to me each morning to show me where the system was breaking. They loved the experience of coming into that crowded, noisy trailer and learning arithmetic and reading skills on the computer. It was clearly an escape for them. I came to realize that there was strong peer pressure against being “into learning” in their classrooms. In the trailer, they were on their own with their computer and they loved the experience of learning and succeeding at the practice challenges we’d created.

What is unique about your work?

I am not a Cyberlearning researcher. At Stanford, I studied artificial intelligence and cognitive science. Since then I wrote a book, co-founded a Silicon Valley startup that went public, and worked with dozens of “advanced tech” software companies around the world in a marketing and business development role. In recent years, I’ve worked on immersive training environments in the military and on international data standards for elearning systems. My role at CIRCL is to help Cyberlearning researchers think about the various ways their work might find its way into products and practice: identifying barriers and ways to address them.

I bring to the Cyberlearning community a unique perspective and a lot of connections with folks in the elearning industry. The recent boom in investment and new elearning products has put some pressure on the Cyberlearning community — why is so little of our research being used to improve products and teaching? The elearning investment boom won’t last forever. This decade is a unique moment in history and this is the time for Cyberlearning to change the way people teach and learn.

What is your most recent insight from your project about cyberlearning?

K12 is hard. For a variety of reasons, classroom-based compulsory education is resistant to change. I have a great deal of respect for the creativity of the teachers and administrators who are exploring all kinds of wonderful classroom innovation, but I think the deck is stacked against them. K12 is not just about education. There are additional social, custodial, and certification objectives for primary schooling that make it hard to bring about radical change in “education” alone — change that realizes the full potential of technology.

Compare the situation in K12 to higher education, where we are likely to see broad changes in the institutions and teaching methodology in the coming decade. Driven by economic and social realities (the socially perceived value of a college education), colleges and universities are all re-thinking their mission and how best to achieve it. As a result, higher-ed is a better environment to explore the use of new technologies to teach in new ways.

What infrastructure would accelerate the cyberlearning community?

I believe that data is the key to success for online learning, especially for adaptive systems that customize each learner’s experience. Smart systems need data about the learner’s history, learning objectives, knowledge, preferences, interests, challenges, and so on — not just grades and test scores, but the kinds of information Cyberlearning researchers put into learner models. This data needs to be shared across systems. Shared data will speed adoption and reduce the setup burden for institutions and teachers. Even more importantly, shared learner data will speed up innovation: many powerful online apps are built on top of the data that other apps generate.

The public concern about learner data security is understandable, but we must consider the potential future benefits of shared data, not just the benefits within the current system of schooling. I believe that learner data should be secured by schools, by product and service providers, and by government agencies, but that access to their data should be controlled by the learner. The risk that someone will be able to hack into a child’s personal learning data must be balanced against the potential impact of new products that use that data to help teachers teach and learners learn.

Publications

Barr, A. & Feigenbaum, E. (1981). The Handbook of Artificial Intelligence, Volume 1. Los Altos, CA: William Kaufman, Inc.

Barr, A., Feigenbaum, E. (1982). The Handbook of Artificial Intelligence, Volume 2. Los Altos, CA: William Kaufman, Inc.

Barr, A., Cohen, P. R., & Feigenbaum, E., & (1990). The Handbook of Artificial Intelligence, Volume 4. Addison-Wesley.

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