@inproceedings{b5f77e331fc54d6593707939a0a00ca9,
title = "Multilingual Short Text Responses Clustering for Mobile Educational Activities: A Preliminary Exploration",
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.",
author = "Tseng, {Yuen Hsien} and Lee, {Lung Hao} and Chien, {Yu Ta} and Chang, {Chun Yen} and Li, {Tsung Yen}",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computational Linguistics.; ACL 2018 5th Workshop on Natural Language Processing Techniques for Educational Applications, NLPTEA 2018 ; Conference date: 19-07-2018",
year = "2018",
language = "???core.languages.en_GB???",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "157--164",
booktitle = "ACL 2018 - Natural Language Processing Techniques for Educational Applications, Proceedings of the 5th Workshop",
}