TY - GEN
T1 - Automated Scoring of Self-explanations Using Recurrent Neural Networks
AU - Panaite, Marilena
AU - Ruseti, Stefan
AU - Dascalu, Mihai
AU - Balyan, Renu
AU - McNamara, Danielle S.
AU - Trausan-Matu, Stefan
N1 - Funding Information:
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.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Comprehensive tutoring system
KW - Natural Language Processing
KW - Recurrent Neural Network
KW - Self-explanations
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U2 - 10.1007/978-3-030-29736-7_61
DO - 10.1007/978-3-030-29736-7_61
M3 - Conference contribution
AN - SCOPUS:85072976599
SN - 9783030297350
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 659
EP - 663
BT - Transforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings
A2 - Scheffel, Maren
A2 - Broisin, Julien
A2 - Pammer-Schindler, Viktoria
A2 - Ioannou, Andri
A2 - Schneider, Jan
PB - Springer Verlag
T2 - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019
Y2 - 16 September 2019 through 19 September 2019
ER -