Authoring expert knowledge bases for intelligent tutors through crowdsourcing

Abstract

We have developed a methodology for constructing domain-level expert knowledge bases automatically through crowdsourcing. This approach involves collecting and analyzing the work of numerous students within an intelligent tutor and using an intelligent algorithm to coalesce data to construct the domain model. This evolving expert knowledge base (EEKB) is then utilized to provide expert coaching and tutoring with future students. We can compare the knowledge created in human crafted expert knowledge bases (HEKB) with knowledge resulting from our knowledge acquisition algorithm to judge quality. We find that our EEKB models have qualities that rival that of the human crafted knowledge bases and can be generated in significantly less time. We have built four unique knowledge bases using this methodology. This paper provides a pithy high-level overview of our approach along with some findings. © 2013 Springer-Verlag Berlin Heidelberg.