A Blended Grammar Learning System Featuring Unsupervised Pattern Discovery


  • Hengbin Yan Guangdong University of Foreign Studies
  • Yinghui Li Guangdong University of Foreign Studies




blended learning, grammar pedagogy, data-driven learning


Recent developments in cognitive and psycholinguistic research postulate that language learning is essentially the learning of grammatical construc-tions. An important type of grammatical construction with wide-ranging pedagogical implications is grammar patterns as laid out in Pattern Gram-mar. While grammar patterns have seen increasing adoption in language pedagogy, existing applications typically follow a paper-based, teacher-centered approach to instruction, which is known to be less effective in grammar learning than blended, learner-centered approaches. In this paper, we propose a blended learning model that integrates web-based technology with classroom-based instruction to facilitate efficient, personalized grammar learning. We present the design and implementation of a blended grammar learning system that provides customizable learning materials for individual learners by discovering important grammar patterns from corpora in an unsupervised manner. Preliminary evaluation shows that the proposed system achieves an accuracy in pattern discovery comparable to systems that rely on manually precompiled pattern lists and hard-coded rules. With a flexible architecture and an easy-to-use interface, the system can play a key role in the creation of a blended learning environment that can be integrated into a wide range of language learning curricula.

Author Biographies

Hengbin Yan, Guangdong University of Foreign Studies

Hengbin Yan is an Associate Professor of Applied Linguistics at Center for Lin-guistics and Applied Linguistics, Guangdong University of Foreign Studies, Guang-zhou City, Guangdong Province, China. He obtained his PhD degree in computation-al and functional linguistics from the City University of Hong Kong, where he also worked as a post-doctoral fellow. He was a visiting scholar at University of California at Berkeley and Lancaster University. His research interests include Computational Linguistics, Corpus Linguistics and Computer-Assisted Language Learning. Most recently, his work has focused on the computational modeling of construction gram-mar in contexts of second language acquisition.

Yinghui Li, Guangdong University of Foreign Studies

Yinghui Li is a Lecturer of Psycholinguistics at Center for Linguistics and Ap-plied Linguistics, Guangdong University of Foreign Studies, Guangzhou City, Guangdong Province, China. She obtained her Ph.D. in Psycholinguistics at Guang-dong University of Foreign Studies. Her research interest is bilingual language pro-cessing, in particular the cognitive and psychological process of interpreting.




How to Cite

Yan, H., & Li, Y. (2021). A Blended Grammar Learning System Featuring Unsupervised Pattern Discovery. International Journal of Emerging Technologies in Learning (iJET), 16(16), pp. 21–34. https://doi.org/10.3991/ijet.v16i16.21857