When Less Is More: Focused Pruning of Knowledge Bases to Improve Recognition of Student Conversation

Mark Floryan, Toby Dragon, Beverly Park Woolf

Expert knowledge bases are effective tools for providing a domain model from which intelligent, individualized support can be offered. This is even true for noisy data such as that gathered from activities involving ill-defined domains and collaboration. We attempt to automatically detect the subject of free-text collaborative input by matching students’ messages to an expert knowledge base. In particular, we describe experiments that analyze the effect of pruning a knowledge base to the nodes most relevant to current students’ tasks on the algorithm’s ability to identify the content of student chat. We discover a tradeoff. By constraining a knowledge base to its most relevant nodes, the algorithm detects student chat topics with more confidence, at the expense of overall accuracy. We suggest this trade-off be manipulated to best fit the intended use of the matching scheme in an intelligent tutor.

The final publication is available at Springer via https://doi.org/10.1007/978-3-642-30950-2_44.