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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publicationACL 2018 - Natural Language Processing Techniques for Educational Applications, Proceedings of the 5th Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages157-164
Number of pages8
ISBN (Electronic)9781948087353
StatePublished - 2018
EventACL 2018 5th Workshop on Natural Language Processing Techniques for Educational Applications, NLPTEA 2018 - Melbourne, Australia
Duration: 19 Jul 2018 → …

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

ConferenceACL 2018 5th Workshop on Natural Language Processing Techniques for Educational Applications, NLPTEA 2018
Country/TerritoryAustralia
CityMelbourne
Period19/07/18 → …

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