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Bring It On! Challenges Encountered While Building a Comprehensive Tutoring System Using 'Readerbench'

Authors :
Marilena Panaite
Mihai Dascalu
Amy Johnson
Renu Balyan
Jianmin Dai
Danielle S. McNamara
Stefan Trausan-Matu
Source :
Grantee Submission. 2018.
Publication Year :
2018

Abstract

Intelligent Tutoring Systems (ITSs) are aimed at promoting acquisition of knowledge and skills by providing relevant and appropriate feedback during students' practice activities. ITSs for literacy instruction commonly assess typed responses using Natural Language Processing (NLP) algorithms. One step in this direction often requires building a scoring mechanism that matches human judgments. This paper describes the challenges encountered while implementing an automated evaluation workflow and adopting solutions for increasing performance of the tutoring system. The algorithm described here comprises multiple stages, including initial pre-processing, a rule-based system for pre-classifying self-explanations, followed by classification using a Support Virtual Machine (SVM) learning algorithm. The SVM model hyper-parameters were optimized using grid search approach with 4,109 different self-explanations scored 0 to 3 (i.e., poor to great). The accuracy achieved for the model was 59% (adjacent accuracy = 97%; Kappa = 0.43). [This paper was published in: "Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part I," (Lecture Notes in Computer Science; Vol. 10947), (Lecture Notes in Artificial Intelligence), pp. 409-419.]

Details

Language :
English
Database :
ERIC
Journal :
Grantee Submission
Publication Type :
Conference
Accession number :
ED637385
Document Type :
Speeches/Meeting Papers<br />Reports - Research
Full Text :
https://doi.org/10.1007/978-3-319-93843-1_30