Personalized question recommendation for English grammar learning

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...

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Main Authors: Fang, Lanting, Tuan, Luu Anh, Hui, Siu Cheung, Wu, Lenan
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2019
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Online Access:https://hdl.handle.net/10356/103685
http://hdl.handle.net/10220/48599
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1036852020-03-07T11:50:49Z Personalized question recommendation for English grammar learning Fang, Lanting Tuan, Luu Anh Hui, Siu Cheung Wu, Lenan School of Computer Science and Engineering Content-based Recommendation DRNTU::Engineering::Computer science and engineering Grammar Question Recommendation 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. Accepted version 2019-06-07T04:15:17Z 2019-12-06T21:17:54Z 2019-06-07T04:15:17Z 2019-12-06T21:17:54Z 2017 Journal Article 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 0266-4720 https://hdl.handle.net/10356/103685 http://hdl.handle.net/10220/48599 10.1111/exsy.12244 en Expert Systems © 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. 13 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Content-based Recommendation
DRNTU::Engineering::Computer science and engineering
Grammar Question Recommendation
spellingShingle Content-based Recommendation
DRNTU::Engineering::Computer science and engineering
Grammar Question Recommendation
Fang, Lanting
Tuan, Luu Anh
Hui, Siu Cheung
Wu, Lenan
Personalized question recommendation for English grammar learning
description 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.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Fang, Lanting
Tuan, Luu Anh
Hui, Siu Cheung
Wu, Lenan
format Article
author Fang, Lanting
Tuan, Luu Anh
Hui, Siu Cheung
Wu, Lenan
author_sort Fang, Lanting
title Personalized question recommendation for English grammar learning
title_short Personalized question recommendation for English grammar learning
title_full Personalized question recommendation for English grammar learning
title_fullStr Personalized question recommendation for English grammar learning
title_full_unstemmed Personalized question recommendation for English grammar learning
title_sort personalized question recommendation for english grammar learning
publishDate 2019
url https://hdl.handle.net/10356/103685
http://hdl.handle.net/10220/48599
_version_ 1681047596390416384