進階搜尋


下載電子全文  
系統識別號 U0026-0212201214253500
論文名稱(中文) 英語閱讀測驗自動化出題系統
論文名稱(英文) A Question Generating System for English Reading Comprehension
校院名稱 成功大學
系所名稱(中) 工程科學系碩博士班
系所名稱(英) Department of Engineering Science
學年度 101
學期 1
出版年 102
研究生(中文) 張富喬
研究生(英文) Fu-Chiao Chang
學號 n96991144
學位類別 碩士
語文別 中文
論文頁數 29頁
口試委員 口試委員-孫光天
口試委員-盧文祥
口試委員-李健興
指導教授-王宗一
中文關鍵字 全民英檢  自然語言處理  題目產生 
英文關鍵字 GEPT  Question Generating  Natural Language Processing 
學科別分類
中文摘要 英語學習中,閱讀一直佔了很重要的一個部分,我們以模擬全民英檢中級閱讀理解測驗的出題方式,希望能提供自主學習者一個驗證閱讀結果的方式。在這篇論文中將介紹我們在研究中所使用的各項自然語言處理工具,並且說明如何利用這些資訊擬定出題策略。我們的研究成果離真正能幫助學習者的自動化題目產生系統仍有一段距離,但是我們在研究的最後提出了一些建議,希望能給將來的研究者一些方向。
英文摘要 Reading is an important part of English learning. We try to make a question generating system to provide GEPT-like reading comprehension for the article they have read, and we believe this kind of systems will help people check whether they understand the article. In the thesis, we introduce some Natural Language Processing tools and show the detail about how we put them in the system. This research still needs lots of effort to achieve the target, but we make some suggestions in the end of the thesis. We believe these suggestions would be helpful.
論文目次 中文摘要 I
Abstract II
目錄 III
圖目錄 IV
表目錄 V
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第三節 研究貢獻 3
第四節 論文架構 4
第二章 相關研究 5
第一節 數位學習發展歷程 5
第二節 英語相關的數位學習 6
第三節 自動出題相關研究 7
第三章 系統設計 9
第一節 相關的語言處理工具 9
第二節 前處理流程 10
第三節 題目產生流程 12
第四章 實驗設計與結果 20
第一節 實驗設計與流程 20
第二節 實驗結果資料分析 23
第五章 結論與建議 26
第一節 結論 26
第二節 建議 27
參考文獻 28
參考文獻 [1] "On the impact of adaptive test question selection for learning efficiency," Computers & Education, pp. 846-857, 2010.
[2] K. C. D. M. B. E. Annabel Latham, "A conversational intelligent tutoring system to automatically predict learning styles," Computers & Education, pp. 95-109, 2012.
[3] C. S. C. Yavuz Akbulut, “Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011,” Computers & Education, pp. 835-842, 2012.
[4] F. Ju-Hsien, English Vocabulary Learning System Based on Memory Cycle Update and Contextual Information, 2009.
[5] L.-C. Huang, A Fuzzy Logic Based Personalized English Document Recommendation System, 2009.
[6] K. C.-T. J. C. M. E. ,. J. L. W. Michael Heilman, “Personalization of Reading Passages Improves Vocabulary Acquisition,” International Journal of Artificial Intelligence in Education, pp. 73-98, 2010.
[7] M.-H. Hsu, “A personalized English learning recommender system for ESL students,” Expert Systems with Applications, pp. 683-688, 2008.
[8] C. H. Patricia Thornton, “Using mobile phones in English education in Japan,” Journal of Computer Assisted Learning, pp. 217-228, 2005.
[9] M. M. K. d. G. Jacobijn Sandberg, “Mobile English learning: An evidence-based study withfifth graders,” Computers & Education, pp. 1334-1347, 2011.
[10] L. A. H. Ruslan Mitkov, “Computer-Aided Generation of Multiple-Choice Tests,” Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2, pp. 17-22 , 2003.
[11] G. A. F. M. E. Jonathan C. Brown, “Automatic Question Generation for Vocabulary Assessment,” HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 819-826, 2005.
[12] C.-L. L. a. Z.-M. G. Shang-Ming Huang, “Computer-Assisted Item Generation for Listening Cloze Tests and Dictation Practice in English,” ICWL 2005, LNCS 3583, pp. 197-208, 2005.
[13] C.-Y. Chen, M. H. Ko, T.-W. Wu 且 J. S. Chang, “FAST: Free Assistant of Structural Tests,” 於 proceedings of the 17th conference on computational linguistics and speech processing, 2005.
[14] J.-F. Y. J.-M. C. J. S. C. Yuan-Chien Yang, “Development of a Computer Assisted Reading Comprehension Test,” 於 International Conference on English Instruction and Assessment, 2006.
[15] L. A. H. a. R. M. Nikiforos Karamanis, “Generating Multiple-Choice Test Items from Medical Text: A Pilot Study,” Proceedings of the Fourth International Natural Language Generation Conference, pp. 111-113 , 2006.
[16] M. A. a. P. Mannem, “Automatic Gap-fill Question Generation from Text Books,” Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications, pp. 56-64 , 2011.
[17] C. S. S. A. I. A. S. Liana Stanescu, “Question generation for learning evaluation,” Proceedings of the International Multiconference on Computer science and Information technology, pp. 509-513, 2008.
[18] R. E. C. O. L. A. H. a. V. P. Ruslan Mitkov, “Anaphora Resolution: To What Extent Does It Help NLP Applications?,” Proceedings of the 6th discourse anaphora and anaphor resolution conference on Anaphora: analysis, algorithms and applications, pp. 179-190, 2007.
[19] D. C. R. M. A. B. Constantin Orasan, “Anaphora Resolution Exercise: An overview,” 於 LREC 2008 Proceedings, 2008.
[20] Y.-T. Lin, Automatic Multiple-choice Question Generation based on Coreference Resolution, 2009.
[21] C.-J. C. Chih-Ming Chen, “Personalized mobileEnglish vocabulary learning system based on item response theory and learning memory cycle,” Computers & Education, pp. 624-645, 2008.
[22] T. K. T. W. T. I. T. Y. Takuya Goto, “Automatic Generation System of Multiple-Choice Cloze Questions and its Evaluation,” 於 Knowledge Management & E-Learning, 2010.
[23] M. Heilman, Automatic Factual Question Generation from Text, 2011.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2015-12-06起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2015-12-06起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw