OWL: A Recommender System for Organization-Wide Learning

Frank Linton, Andy Charron, and Debbie Joy

We describe the use of a recommender system to enable continuous knowledge acquisition and individualized tutoring of application software across an organization. Installing such systems will result in the capture of evolving expertise and in organization-wide learning (OWL). We present the results of a year-long naturalistic inquiry into application’s usage patterns, based on logging users’ actions. We analyze the data to develop user models, individualized expert models, confidence intervals, and instructional indicators. We show how this information could be used to tutor users.

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