Abstract
Question classification is an important part of several tasks, especially in educational systems. In this paper, we present a system that classifies questions asked in an educational context based on the expected characteristics of answers, with a future goal to facilitate the analysis of student responses. To this end, we propose an approach that employs a deep neural network together with features tailored to each question type (expected answer characteristic), word embeddings and inter-class dependency features. To demonstrate the effectiveness of the proposed method, we use a corpus of questions collected from real science classrooms and augment it with data from online resources from the same domain. Our approach achieves a weighted \(F_1\)-score of 0.678, outperforming the baseline by 56%.
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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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Godea, A., Tulley-Patton, D., Barbee, S., Nielsen, R. (2018). Classifying Educational Questions Based on the Expected Characteristics of Answers. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_20
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DOI: https://doi.org/10.1007/978-3-319-93846-2_20
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