Abstract
In the US in particular, there is an increasing emphasis on the importance of science in education. To better understand a scientific topic, students need to compile information from multiple sources and determine the principal causal factors involved. We describe an approach for automatically inferring the quality and completeness of causal reasoning in essays on two separate scientific topics using a novel, two-phase machine learning approach for detecting causal relations. For each core essay concept, we initially trained a window-based tagging model to predict which individual words belonged to that concept. Using the predictions from this first set of models, we then trained a second stacked model on all the predicted word tags present in a sentence to predict inferences between essay concepts. The results indicate we could use such a system to provide explicit feedback to students to improve reasoning and essay writing skills.
D. Blaum—The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305F100007 to University of Illinois at Chicago. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.
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Hughes, S., Hastings, P., Britt, M.A., Wallace, P., Blaum, D. (2015). Machine Learning for Holistic Evaluation of Scientific Essays. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_17
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