Abstract
Intelligent Tutoring Systems (ITSs) focus on promoting knowledge acquisition, while providing relevant feedback during students’ practice. Self-explanation practice is an effective method used to help students understand complex texts by leveraging comprehension. Our aim is to introduce a deep learning neural model for automatically scoring student self-explanations that are targeted at specific sentences. The first stage of the processing pipeline performs an initial text cleaning and applies a set of predefined rules established by human experts in order to identify specific cases (e.g., students who do not understand the text, or students who simply copy and paste their self-explanations from the given input text). The second step uses a Recurrent Neural Network with pre-trained Glove word embeddings to predict self-explanation scores on a scale of 1 to 3. In contrast to previous SVM models trained on the same dataset of 4109 self-explanations, we obtain a significant increase of accuracy from 59% to 73%. Moreover, the new pipeline can be integrated in learning scenarios requiring near real-time responses from the ITS, thus addressing a major limitation in terms of processing speed exhibited by the previous approach.
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Acknowledgments
This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI, project number PN-III 72PCCDI ⁄ 2018, ROBIN – “Roboții și Societatea: Sisteme Cognitive pentru Roboți Personali și Vehicule Autonome”, the Department of Education, Institute of Education Sciences - Grant R305A130124 and R305A190063, and the Department of Defense, Office of Naval Research - Grants N00014140343 and N000141712300.
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Panaite, M., Ruseti, S., Dascalu, M., Balyan, R., McNamara, D.S., Trausan-Matu, S. (2019). Automated Scoring of Self-explanations Using Recurrent Neural Networks. In: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds) Transforming Learning with Meaningful Technologies. EC-TEL 2019. Lecture Notes in Computer Science(), vol 11722. Springer, Cham. https://doi.org/10.1007/978-3-030-29736-7_61
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DOI: https://doi.org/10.1007/978-3-030-29736-7_61
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