Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration

Yuen-Hsien Tseng, Lung-Hao Lee, Yu-Ta Chien, Chun-Yen Chang, Tsung-Yen Li


Abstract
Text clustering is a powerful technique to detect topics from document corpora, so as to provide information browsing, analysis, and organization. On the other hand, the Instant Response System (IRS) has been widely used in recent years to enhance student engagement in class and thus improve their learning effectiveness. However, the lack of functions to process short text responses from the IRS prevents the further application of IRS in classes. Therefore, this study aims to propose a proper short text clustering module for the IRS, and demonstrate our implemented techniques through real-world examples, so as to provide experiences and insights for further study. In particular, we have compared three clustering methods and the result shows that theoretically better methods need not lead to better results, as there are various factors that may affect the final performance.
Anthology ID:
W18-3723
Volume:
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | NLP-TEA | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
157–164
Language:
URL:
https://www.aclweb.org/anthology/W18-3723
DOI:
10.18653/v1/W18-3723
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-3723.pdf