Peter Hastings, Simon Hughes, Anne Britt, Dylan Blaum, Patty Wallace
With an increasing focus on science and technology in education comes an awareness that students must be able to understand and integrate scientific explanations from multiple sources. As part of a larger project aimed at deepening our understanding of student processes for integrating multiple sources of information, we are developing machine learning and natural language processing techniques for evaluating students’ argumentative essays. In previous work, we have focused on identifying conceptual elements of the essays. In this paper, we present a method for inferring the causal structure of student essays. We used a standard parser to derive grammatical dependencies of the essay and converted them to logic statements. Then a simple inference mechanism was used to identify concepts linked to syntactic connectors by these dependencies. The results suggest that we will soon be able to provide explicit feedback that enables teachers and students to improve comprehension.
The final publication is available at Springer via https://doi.org/10.1007/978-3-319-07221-0_33.