Abstract
This paper presents a pilot study on an intelligent tutoring system for domain-independent argument making. Students' responses to an open-ended question were collected as the instances for supervised text classification based on the grade given by the instructor using structured outcome of the learning observation taxonomy. The responses were processed using Cohmetrix as well as n-gram models to generate attributes for the classification task. The best result of 81.74% in classification correct rate was obtained when all grade classes were used.
Original language | English (US) |
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Title of host publication | Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 |
Editors | Cesar Ferri, Guangzhi Qu, Xue-wen Chen, M. Arif Wani, Plamen Angelov, Jian-Huang Lai |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 553-556 |
Number of pages | 4 |
ISBN (Electronic) | 9781479974153 |
DOIs | |
State | Published - Feb 5 2014 |
Externally published | Yes |
Event | 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 - Detroit, United States Duration: Dec 3 2014 → Dec 6 2014 |
Other
Other | 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 |
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Country/Territory | United States |
City | Detroit |
Period | 12/3/14 → 12/6/14 |
Keywords
- intelligent tutoring systems; arguments; Cohmetrix; text classification
ASJC Scopus subject areas
- Computer Science Applications
- Human-Computer Interaction