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Title: Personalized question recommendation for English grammar learning
Authors: Fang, Lanting
Tuan, Luu Anh
Hui, Siu Cheung
Wu, Lenan
Keywords: Content-based Recommendation
DRNTU::Engineering::Computer science and engineering
Grammar Question Recommendation
Issue Date: 2017
Source: Fang, L., Tuan, L. A., Hui, S. C., & Wu, L. (2018). Personalized question recommendation for English grammar learning. Expert Systems, 35(2), e12244-. doi:10.1111/exsy.12244
Series/Report no.: Expert Systems
Abstract: Learning English grammar is a very challenging task for many students especially for nonnative English speakers. To learn English well, it is important to understand the concepts of the English grammar with lots of practise on exercise questions. Previous recommendation systems for learning English mainly focused on recommending reading materials and vocabulary. Different from reading material and vocabulary recommendations, grammar question recommendation should recommend questions that have similar grammatical structure and usage to the question of interest. The content similarity calculation methods used in existing recommendation methods cannot represent the similarity between grammar questions effectively. In this paper, we propose a content‐based approach for personalized grammar question recommendation, which recommends similar grammatical structure and usage questions for further practising. Specifically, we propose a novel structure named parse‐key tree to capture the grammatical structure and usage of grammar questions. We then propose 3 measures to compute the similarity between the question query and database questions for grammar question recommendation. Additionally, we incorporated the proposed recommendation method into a Web‐based English grammar learning system and presented its performance evaluation in this paper. The experimental results have shown that the proposed approach outperforms other classical and state‐of‐the‐art methods in recommending relevant grammar questions.
ISSN: 0266-4720
Rights: © 2017 John Wiley & Sons, Ltd. All rights reserved. This paper was published in Expert Systems and is made available with permission of John Wiley & Sons, Ltd.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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