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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 孫雅麗(Yeali S. Sun) | |
| dc.contributor.author | Hsiao-Pei Chang | en |
| dc.contributor.author | 張筱珮 | zh_TW |
| dc.date.accessioned | 2021-06-13T07:48:31Z | - |
| dc.date.available | 2016-08-05 | |
| dc.date.copyright | 2011-08-05 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-07-21 | |
| dc.identifier.citation | [1] Chall, J. S. and Dale, E. 1995. Readability Revisited: The New Dale-Chall Readability Formula. In Brookline Books. Cambridge, MA.
[2] Chen, Chun-Yin (Ed.) 2010. Far East High School English Textbooks Level 1: 99 New Course Standards. Taipei: The Far East Book Company. [3] Chen, Y. H., Cheng, C. H. and Liu, J. W.. 2010. Intelligent preference selection model based on NRE for evaluating student learning achievement. In Computer and Education, pp. 916-926 [4] Collins-Thompson, K. and Callan, J. 2005. Predicting reading difficulty with statistical language models. Journal of the American Society for Information Science and Technology, Vol.56, No. 13, pages 1448-1462. [5] Corbett, A., Anderson, J. 1995. Knowledge tracing: Modeling the acquisition of procedural knowledge. In User modeling and user-adapted interaction 4, pages 253–278 [6] Dale and Chall J. S. 1948. A Formula for Predicting Readability. In Educational Research Bulletin Vol. 27, No. 1. [7] Heilman, M., Collins-Thompson, K., Callan, J., and Eskenazi, M. 2007. Combining Lexical and Grammatical Features to Improve Readability Measures for First and Second Language Texts. In Proceedings of the Human Language Technology Conference. [8] Heilman, M. , Collins-Thompson, K. and Eskenazi, M. 2008. An Analysis of Statistical Models and Features for Reading Difficulty Prediction. In Proceedings of the Third ACL Workshop on Innovative Use of NLP for Building Educational Applications, pages 71–79. [9] Hoshino, A. and Nakagawa, H. 2007. Sakumon: An assistance system for English cloze test In Society for Information Technology & Teacher Education International Conference [10] Hoshino, A. and Nakagawa, H. 2010. Predicting the Difficulty of Multiple-Choice Cloze Questions for Computer-Adaptive Testing. In CICLING 2010 Special issue : Natural Language Processing and its Applications [11] Kidwell P., Lebanon G. and Collins-Thompson K..2009. Statistical estimation of word acquisition with application to readability prediction. In Proceedings of Empirical Methods in Natural Language Processing (EMNLP, 2009). [12] Kincaid J., Fishburne R., Rodgers R., and Chissom B.. 1975. Derivation of new readability formulas for navy enlisted personnel. In Branch Report pages 8-75. Chief of Naval Training, Millington, TN. [13] Kunichika H,, Urushima M., Hirashima T. and Takeuchi A. 2002. A Computational Method of Complexity of Questions on Contents of English Sentences and its Evaluation. In Proceedings of the International Conference on Computers in Education (ICCE 2002 ) , pp.97-101. [14] Landauer T. K, Kireyev K. and Panaccione C. 2009. A New Yardstick and Tool for Personalized Vocabulary Building. In Proceedings of the NAACL HLT Workshop on Innovative Use of NLP for Building Educational Applications, pages 27–33 [15] Lin, S. D., Lin, H. T., and Lin, C. J. 2010. Feature Engineering and Classifier Ensemble for KDD Cup 2010. In Proceedings the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1-16. [16] Lin, Y. C., Sung, L. C., and Chen, M. C. 2007. An Automatic Multiple-Choice Question Generation Scheme for English Adjective Understanding. In Proceedings of the 15th International Conference on Computers in Education (ICCE 2007), pages 137-142. [17] Lin, Y. T., Chen, M. C. and Sun, Y. S. 2009. Automatic Text-Coherence Question Generation Based on Coreference Resolution. In Procedding of the 17th International Conference on Computers in Education. [18] Liu Feng, Du Peng, Weng Fangfei, Qu Jun. 2007. Use clustering to improve neural network in financial time series prediction. In Proceedings of the Third International Conference on Natural Computation (ICNC 2007) [19] Mitkov, R. and Ha, L.A. 2003. Computer-Aided Generation of Multiple-Choice Tests. In Workshop on Building Educational Applications Using Natural Language Processing, HLT-NAACL [20] Pavlik, P.I., Cen, H., Koedinger, K. 2009. Performance Factors Analysis - A New Alternative to Knowledge. In Proceedings the 14th International Conference on Artificial Intelligence in Education, Brighton, UK, pages 531–538 [21] Pino,J., Heilman, M., and Eskenazi, M. 2008. A Selection Strategy to Improve Cloze Question Quality. In Proceedings of the Ninth International Conference on Intelligent Tutoring System. [22] Schwarm, S. and Ostendorf, M. 2005. Reading level assessment using support vector machines and statistical language models. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. [23] Stenner, J. 1996. Measuring reading comprehension with the Lexile framework. In Fourth North American Conference on Adolescent/Adult Literacy. [24] Turney, P. D. 2008. A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations. In the Proceedings of the 22nd International Conference on computational Linguistics, pages 905-912 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/35940 | - |
| dc.description.abstract | 過去自動出題系統相關研究的出題方式是窮盡所有可能考題的產生方法，僅有少數研究會針對題目的困難程度進行出題的考量，甚至鮮少考慮使用者程度以及文章本身閱讀困難度。因此本研究提出以文章的閱讀困難度與學習者的英語程度估計為基礎，為第二語言學習者設計了一個個人化的自動出題系統。在閱讀困難度估計方面，我們採用一些過去研究中較有意義及代表性的詞彙和語法特徵，然後再加入考量幾個不同特徵來分析一篇文章的可讀性，分別是語料庫中單字出現頻率、語言專家所制定的單字官方分及索引及從不同版本的高中教課書中整理出的文法模式 - 那些單字和文法的困難度代表第二語言學習者會學會該單字或文法的年級。在學生英語程度預測方面，我們從三個層面去估計一個學習者的能力，分別是字彙能力、文法能力，以及閱讀理解能力，然後再利用指數移動平均線去考慮他的歷史表現以確定學習者的英語程度水平。我們提供學生個人化的新聞閱讀與測驗活動，實驗結果顯示本研究所提供的閱讀困難度估計模型的結果優於其他的方法，且本研究所估計的文章閱讀困難度也接近專家的看法；並且學習者在使用我們的個人化閱讀和測驗系統後能顯著提升他們的英語程度且系統可準確預測出學習者的程度。 | zh_TW |
| dc.description.abstract | A lot of research works have been done in the field of automatic quiz generation, however, almost all those studies generate all possible combination of quizzes and only few research consider difficulties of different quizzes. In this study, not only a quiz‘s difficulty but also the difference between learners and the reading difficulty of a document are taken into consideration. Therefore, we design a personalized automatic quiz generation system based on reading difficulty estimation scheme in a given document and proficiency level prediction for a second language learner. In the reading difficulty estimation scheme, we consult some meaningful lexical and grammatical features in early work, and then further consider several word frequency features from corpora, official grading indexes of vocabulary from language experts, and grammar patterns collected from textbooks — those which represent words and grammar patterns that the L2 learners have learned at various grade levels. In the proficiency level prediction, we estimate a learner‘s ability from three dimensions, which are vocabulary ability, grammar ability, and reading comprehension ability, and then further consider his historical performance to determine his proficiency level by weighted exponential moving average. A personalized news reading and testing experiment was conducted. The experimental results show that the proposed estimation outperforms the other estimations, and is close to the annotation of human experts. Moreover, It also shows that our system can increase learners‘ English proficiency, and provide a good prediction of learners‘ proficiency level. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T07:48:31Z (GMT). No. of bitstreams: 1 ntu-100-R98725037-1.pdf: 1196487 bytes, checksum: 826f776b31220787e8176d5db6f45aec (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 論文摘要 ............................................................................................................................................ iv
THESIS ABSTRACT ......................................................................................................................... v Table of Contents ............................................................................................................................... vi List of Figures ................................................................................................................................. viii List of Tables ..................................................................................................................................... ix Chapter 1 Introduction ...................................................................................................................... 1 1.1 Background ...................................................................................................................... 1 1.2 Motivation and Objective ................................................................................................. 2 1.3 Thesis Structure................................................................................................................ 3 Chapter 2 Related Works .................................................................................................................. 4 2.1 Reading Difficulty Assessment ........................................................................................ 4 2.2 Proficiency Level Prediction ............................................................................................ 7 Chapter 3 Reading Difficulty Estimation ...................................................................................... 11 3.1 The L2 Reading Difficulty Estimation Scheme ............................................................. 11 3.2 Lexical Features ............................................................................................................. 12 3.3 Grammatical Features .................................................................................................... 14 3.4 Semantic features ........................................................................................................... 17 3.5 Statistical models ........................................................................................................... 18 Chapter 4 Proficiency Level Estimation ........................................................................................ 19 4.1 Proficiency Level Estimation Model ............................................................................. 19 4.1.1 Learner‘s Ability Estimation .............................................................................. 20 4.1.2 Proficiency Level Prediction .............................................................................. 21 4.1.3 Correct Rate Prediction ...................................................................................... 22 4.2 Personalized Automatic Quiz Generation ...................................................................... 24 4.2.1 Question‘s Difficulty Estimation ....................................................................... 24 4.2.2 Quiz Generation Strategy ................................................................................... 25 4.2.3 Learner‘s Proficiency and Test‘s Difficulty Adjustment Approach .................. 26 Chapter 5 Experiments .................................................................................................................... 27 5.1 Implemental System ....................................................................................................... 27 5.2 Reading Difficulty Estimation Experiment Settings ...................................................... 29 5.3 Reading Difficulty Estimation Features Analysis .......................................................... 31 5.3.1 Lexical Features Analysis .................................................................................. 32 5.3.2 Grammatical Features Analysis ......................................................................... 33 5.3.3 Semantic Features Analysis ............................................................................... 35 5.4 Reading Difficulty Estimation Experiment Results ....................................................... 36 5.4.1 Baseline Methods ............................................................................................... 36 5.4.2 Results ................................................................................................................ 37 5.4.3 Reading Difficulty Optimum Model .................................................................. 40 5.5 Proficiency Level Estimation Experiment Settings ....................................................... 41 5.6 Proficiency Level Estimation Experiment Results ........................................................ 42 5.6.1 Significant Progress in Post-test Score .............................................................. 43 5.6.2 Learner‘s Level Changes in Twelve Tests ......................................................... 44 5.6.3 Correlation between Predicted Level and Post-test Score ................................. 48 5.6.4 Correlation between Pre-test Score and Test Score in School ........................... 49 Chapter 6 Discussion ....................................................................................................................... 51 6.1 New Metric to evaluate the Reading Difficulty Estimation ........................................... 52 6.2 New Proficiency Level Prediction Model ...................................................................... 54 6.3 New Method to Estimate the Correct Rate .................................................................... 55 6.4 Alpha Value Improvement ............................................................................................. 57 Chapter 7 Conclusion and Future works ....................................................................................... 59 References ........................................................................................................................................ 61 Appendix A: grammar patterns in our study ................................................................................ 65 | |
| dc.language.iso | en | |
| dc.subject | 指數移動平均線 | zh_TW |
| dc.subject | 可讀性 | zh_TW |
| dc.subject | 閱讀困難度 | zh_TW |
| dc.subject | 第二語言學習 | zh_TW |
| dc.subject | 線性迴歸模型 | zh_TW |
| dc.subject | 適性化測驗 | zh_TW |
| dc.subject | 自動出題系統 | zh_TW |
| dc.subject | 個人化 | zh_TW |
| dc.subject | linear regression model | en |
| dc.subject | exponential moving average(EMA) | en |
| dc.subject | personalization | en |
| dc.subject | automatic quiz generation | en |
| dc.subject | readability | en |
| dc.subject | reading difficulty | en |
| dc.subject | second language learning | en |
| dc.title | 以閱讀困難度與預測學生英語程度為基礎之個人化自動出題 | zh_TW |
| dc.title | Personalized Automatic Quiz Generation Based on Reading Difficulty and Proficiency Level Prediction | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 陳孟彰(Meng Chang Chen) | |
| dc.contributor.oralexamcommittee | 魏友賢(David Wible),陳建錦(Chien Chin Chen),楊接期(Jie Chi Yang) | |
| dc.subject.keyword | 可讀性,閱讀困難度,第二語言學習,線性迴歸模型,適性化測驗,自動出題系統,個人化,指數移動平均線, | zh_TW |
| dc.subject.keyword | readability,reading difficulty,second language learning,linear regression model,automatic quiz generation,personalization,exponential moving average(EMA), | en |
| dc.relation.page | 68 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-07-22 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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