Collaboration and Content Recognition Features in an Inquiry Tutor

This demonstration will show how a tutor can detect the content of collaborative behavior and offer relevant domain level interventions. Rashi is a domain independent intelligent tutor providing students with practice using inquiry skills. When working on human biology, students interact with a virtual sick patient whom they must successfully diagnose. Rashi supports students as they create hypotheses and collect data to support and refute these hypotheses. In order to increase the efficacy of Rashi, we incorporated collaborative tools that support group efforts by supporting students as they dynamically share experiences and work together to reach a diagnosis. In addition to this, Rashi contains an intelligent agent that examines collaborative efforts and automatically detects the expert knowledge students are working with. Visitors to this demo will first explore these collaborative tools in detail. Two people will collaborate about a diagnosis and the intelligent agent will examine their collaborative activity and compare it with an expert knowledge base, to determine what domain content is relevant to their activities. The tutor will provide interventions to the visitors that leverage this content recognition.

This demonstration provides evidence that expert knowledge bases are a plausible development option for intelligent tutoring systems because they can leverage content recognition to provide more useful feedback. In Rashi, some collaborative content is recognized when students manually match discussion items to expert knowledge. However, the greatest impact comes when the tutor recognizes participants’ content by matching words and phrases in the chat conversation. Experiments show that the tutor can recognize this content correctly with more than 70% accuracy. Thus, it can provide interventions that suggest what direction students might take if they reached an impasse.

This demonstration provides evidence that complicated NLP techniques are not always necessary; a tutor can understand domain level student activity and provide useful interventions using a well-built expert knowledge base. In addition, we show that even though the lack of more complicated techniques may lead to some error in content recognition, we can provide unique forms of feedback that are not detrimental to students when the content is incorrectly recognized, but is significantly helpful when it is correctly recognized.