Abstract
In an intelligent educational system, automatic sentence alignment has a pivotal role in determining a foundation for clustering, comparing, summarizing and classifying responses. In this paper, we go beyond sentence alignment by splitting the reference and the student responses into single clauses, which are then aligned using fine-grained semantic components (facets). This detailed analysis will enable automated educational systems to become highly scalable, domain-independent and to enrich the classroom experience. The results are very promising, showing a significant increase in terms of \(F_1\)-score, compared to the best performing baseline.
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Acknowledgements
This research was supported by the Institute of Education Sciences, U.S. Department of Education, Grant R305A120808 to University of North Texas. Opinions expressed are those of the authors.
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Bulgarov, F., Nielsen, R. (2017). Minimal Meaningful Propositions Alignment in Student Response Comparisons. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_43
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DOI: https://doi.org/10.1007/978-3-319-61425-0_43
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