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Recommender Systems for Learning: Building User and Expert Models through Long-Term Observation of Application Use

  • Frank Linton
  • Hans-Peter Schaefer
Article

Abstract

Information technology has recently become the medium in which much professional office work is performed. This change offers an unprecedented opportunity to observe and record exactly how that work is performed. We describe our observation and logging processes and present an overview of the results of our long-term observations of a number of users of one desktop application. We then present our method of providing individualized instruction to each user by employing a new kind of user model and a new kind of expert model. The user model is based on observing the individual's behavior in a natural environment, while the expert model is based on pooling the knowledge of numerous individuals. Individualized instructional topics are selected by comparing an individual's knowledge to the pooled knowledge of her peers.

agent cluster analysis data mining instructional technology instrumentation knowledge acquisition learning logging long-term observation organizational learning OWL recommender system sequence analysis student modeling 

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Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Frank Linton
    • 1
  • Hans-Peter Schaefer
    • 1
  1. 1.The MITRE CorporationBedfordUSA

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