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系統識別號 U0002-1001201921002900
中文論文名稱 非英語為母語的學習者之人稱代名詞使用:資料庫研究
英文論文名稱 Personal Pronoun We and Other Key Items in Non-Native English Learners Academic Writing: A Corpus-Driven Study
校院名稱 淡江大學
系所名稱(中) 英文學系博士班
系所名稱(英) Department of English
學年度 107
學期 1
出版年 108
研究生中文姓名 鄭玉成
研究生英文姓名 Ngoc-Thanh Trinh
學號 803110096
學位類別 博士
語文別 英文
口試日期 2019-01-11
論文頁數 119頁
口試委員 指導教授-王藹玲
委員-林裕昌
委員-蔡麗娟
委員-張慈珊
委員-徐小惠
中文關鍵字 語料庫驅動  人稱代名詞  字頻  混合方法  學術寫作 
英文關鍵字 corpus-driven  personal pronoun  word frequency  mixed methods  academic writing 
學科別分類
中文摘要 本研究運用語料庫驅動的研究方法(corpus-driven approach)主要分析非英語母語學習者的學術寫作。根據三角剖分的方法論點(triangulation),本研究首先采用混合方法研究設計(mixed methods research design) 來研究指定的語言對象。 其次,研究方向透過量化分析提出語言模式既而結合質性分析解釋量化結果. 第三,在結合量化結果和質性解釋當中,混合研究設計旨在增加研究結果的可靠性(reliability)。
本研究研究重點包含第一人稱代名詞we及其他詞彙之使用。學術寫作文本來自 2015年ICNALE SW 1.1,其中作者提取中國學習者,其他非英語母語的學習者(包含泰國,日本,韓國學習者)和一組英語母語的學習者。 與此同時,研究範圍分為兩部分:(1)屬於中國學習者語料庫者分析we過度之使用和we句子; (2)屬於多種學習者的語料庫觀察人稱代名詞(包含第一,第二第三人稱代名詞) 和第一人稱代名詞(I & we)n-grams。研究意義提出基於字頻統計分析(frequency-based analysis)是語料庫驅動的研究方法(corpus-driven approach)重要的元素。

英文摘要 The present study implements corpus-driven approach to analyze non-native English learners academic writing. On the notion of triangulation, this study uses mixed methods research design to study the specified linguistic objects. In particular, the research direction firstly goes through quantitative analysis to propose the linguistic model before explaining quantitative results in combination with qualitative analysis. In the combination of quantitative results and qualitative interpretation, the mixed methods research design aims to increase the reliability of the research results.

The focus in this study includes first person plural pronoun we and other key items. The academic writing text data is from ICNALE SW 1.1 (2015) in which the author extracted the written modules of Chinese, other non-native English learners (including Thai, Japanese, and Korean learners), and a group of native English learners. The scope of investigation is divided into two sections: (i) the analysis of excessive use of we and we-clause by Chinese learners and (ii) the analysis of various personal pronouns (including first, second, and third person pronouns) and first person pronouns (I and we) n-grams from multiple learner corpora. The research findings draw on frequency-based analysis to suggest important elements for corpus-driven approach

論文目次 TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION 1
1.1 Research Purposes 1
1.2 Research Background 2
1.3 Statement of the Problems 10
1.4 Research Summary 15
1.5 Research Contributions 17

CHAPTER 2: LITERATURE REVIEW 19
2.1 Research Paradigms in Corpus Linguistics 19
2.2 Research Implementations of Corpus-Driven Approach 24
2.3 Empirical Corpus Findings on Person Pronouns 29

CHAPTER 3: RESEARCH METHODOLOGY 36
3.1 Review of Methods of Investigation 36
3.2 Review of Research Issues 38
3.3 Research Procedures 40
3.4 Pilot study 47

CHAPTER 4: FINDINGS 55
4.1 The Overuse of We in Chinese Learner’s English Essay Corpus 55
4.2 The Distribution of We-Clause in Chinese Learner’s English Essay Corpus 63
4.3 Person Pronouns from Multiple Corpora Comparison 67
4.4 Exploring I and We Tri-Grams from Multiple Corpora Sources 72

CHAPTER 5: DISCUSSION 76
5.1 Patterns of Pronoun Usage 76
5.2 Patterns of Lexical Items Usage 84
5.3 Patterns of n-grams 87

CHAPTER 6: CONCLUSION 96
6.1 Efficiency of Frequency-Based Findings 96
6.2 Limitations of the Study 97
6.3 Direction for Future Studies 99

REFERENCES 101
APPENDIX 117

LIST OF FIGURES
Figure 1: Methodological triangulation for frequency data (form & presentation) and data analysis 29
Figure 2: Top 20 high-frequency word list of the pilot study 49
Figure 3: N-gram of we 51
Figure 4: Pattern of we 51
Figure 5: Top 50 high-frequency word list in Study 4.1 57
Figure 6: The scatter plot presentation for areas of frequency of we in Study 4.1 59
Figure 7: First, second, and third singular and plural pronouns in Study 4.3 70
Figure 8: The scatter plot presentation on pronoun usage of CHN learners in Study 4.1 77
Figure 9: The scatter plot presentation of key items in top 50 high-frequency word list in Study 4.1 84
Figure 10: Extracted n-grams data of money and society in Study 4.1 86
Figure 11: Frequent tri-grams of I, you, and they from THA and ENS learners in Study 4.3 88
Figure 12: Concordance data of I think it on the writing topic about smoking in Study 4.4 90
Figure 13: Concordance data of I think that and I agree with from CHN learners in Study 4.4 92
Figure 14: Concordance data of We can get and We can learn from CHN learners in Study 4.4 94
Figure 15: Concordance data of We can get and We can learn from JPN learners in Study 4.4 95

LIST OF TABLES
Table 1: An outline of corpus-driven studies in the dissertation 46
Table 2: Frequency data of writing samples in the pilot study 48
Table 3: Normalized frequency data of we and part-time job(s) 50
Table 4: 2 x 2 contingency table of we-clause and its indexes 52
Table 5: 2x2 contingency table on observed frequency of we-clause and its index 53
Table 6: The descriptive value distribution for frequency of we in Study 4.1 58
Table 7: The z-score distribution for frequency of we in Study 4.1 after outliers excluded 58
Table 8: One-sample KS tests examining normalized frequency of we into areas of word count level 62
Table 9: Distribution of frequency of we into four word count levels 63
Table 10: 2x2 contingency table on expected frequency (left) and Pearson residuals (right) 65
Table 11: One sample KS test and Mann-Whitney U tests on testing the mean difference of we-clause 66
Table 12: Corpus data of CHN, JPN, KOR, THA, and ENS learners in Study 4.3 67
Table 13: Two predictive models for the difference in pronoun usage between THA and ENS learners 71
Table 14: Corpus data of CHN, JPN, and THA learners in Study 4.4 72
Table 15: I tri-gram data from corpus data of CHN, JPN, and THA learners 74
Table 16: We tri-gram data from corpus data of CHN, JPN, and THA learners 75
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