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<Paper uid="W03-0210">
  <Title>A Hybrid Text Classi cation Approach for Analysis of Student Essays</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusion and Current Directions
</SectionTitle>
    <Paragraph position="0"> In this paper we have introduced the CarmelTC text classi cation approach as it is applied to the problem of student essay analysis in the context of a conceptual physics tutoring system. We have evaluated CarmelTC over data collected from students interacting with our system in response to one of its 10 implemented conceptual physics problems. Our evaluation demonstrates that the novel hybrid CarmelTC approach outperforms both Latent Semantic Analysis (LSA) (Landauer et al., 1998; Laham, 1997) and a Naive Bayes approach (McCallum, 1996; McCallum and Nigam, 1998) as well as a purely symbolic approach similar to (Furnkranz et al., 1998). We plan to run a larger evaluation with essays from multiple problems to test the generality of our result. We also plan to experiment with other rule learning approaches, such as RIPPER (Cohen, 1995).</Paragraph>
  </Section>
class="xml-element"></Paper>