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Title:
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EXAMINING LANGUAGE-AGNOSTIC METHODS OF AUTOMATIC CODING IN THE COMMUNITY OF INQUIRY FRAMEWORK |
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Author(s):
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Yuta Taniguchi, Shinichi Konomi and Yoshiko Goda |
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ISBN:
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978-989-8533-93-7 |
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Editors:
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Demetrios G. Sampson, Dirk Ifenthaler, Pedro Isaías and Maria Lidia Mascia |
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Year:
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2019 |
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Edition:
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Single |
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Keywords:
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CSCL, CoI Framework, Coding, NLP, Prediction |
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Type:
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Full Paper |
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First Page:
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19 |
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Last Page:
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26 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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This study discusses the automatic coding methods of the Community of Inquiry (CoI) framework for multilingual
contexts, in particular. In universities, foreign students cannot be overlooked, and learning systems are also required to
work in multilingual situations. However, none of the existing work has addressed the lack of language-agnostic and
automatic coding algorithms for the CoI framework, even though the framework is widely used to assess
student-generated texts. In this study, we investigate the performance of a data-driven text tokenization algorithm for
automatic coding. Using a real-world dataset, we compare the prediction performance of the language-independent
tokenizer with a language-dependent tokenizer. Our experiments show the data-driven tokenizer to be comparable to its
competitor, and a classification algorithm with this tokenizer could achieve high prediction performance for many CoI
indicators. We believe that our experimental results are informative and could provide a baseline for future research. |
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