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...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/103685 http://hdl.handle.net/10220/48599 |
| _version_ | 1811697635062448128 |
|---|---|
| author | Fang, Lanting Tuan, Luu Anh Hui, Siu Cheung Wu, Lenan |
| 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 |
| author_sort | Fang, Lanting |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T07:58:23Z |
| format | Journal Article |
| id | ntu-10356/103685 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:58:23Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | Personalized question recommendation for English grammar learning |
| title_sort | personalized question recommendation for english grammar learning |
| topic | Content-based Recommendation DRNTU::Engineering::Computer science and engineering Grammar Question Recommendation |
| url | https://hdl.handle.net/10356/103685 http://hdl.handle.net/10220/48599 |
| work_keys_str_mv | AT fanglanting personalizedquestionrecommendationforenglishgrammarlearning AT tuanluuanh personalizedquestionrecommendationforenglishgrammarlearning AT huisiucheung personalizedquestionrecommendationforenglishgrammarlearning AT wulenan personalizedquestionrecommendationforenglishgrammarlearning |