系統識別號 | U0002-2202200718464200 |
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DOI | 10.6846/TKU.2007.00654 |
論文名稱(中文) | 語言輸入對台灣英語學習者英語語塊習得的影響 |
論文名稱(英文) | EFFECTS OF INPUT IN THE ACQUISITION OF FORMULAIC SEQUENCES BY EFL LEARNERS IN TAIWAN |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 英文學系博士班 |
系所名稱(英文) | Department of English |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 95 |
學期 | 1 |
出版年 | 96 |
研究生(中文) | 陳惠如 |
研究生(英文) | Hui-Ju Chen |
學號 | 890010043 |
學位類別 | 博士 |
語言別 | 英文 |
第二語言別 | |
口試日期 | 2007-01-22 |
論文頁數 | 165頁 |
口試委員 |
指導教授
-
衛友賢
委員 - 柯華威 委員 - 郭志華 委員 - 劉賢軒 委員 - 范瑞玲 委員 - 衛友賢 |
關鍵字(中) |
英語語塊 語言輸入 資料驅動學習 發現式學習 第二語言習得 |
關鍵字(英) |
formulaic sequences language input data-driven learning discovery learning second language acquisition |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
隨著電腦科技的發展,英語中的連續語塊逐漸受到語言學習與認知領域的重視,特別是語料庫相關的應用研究,更加強了一項重要的發現,那就是,自然語言的使用與儲存其實包含了許多連續性語塊,然而,就外語學習的研究來看,此方面的實證研究並不多見,特別是將其理論與發現運用在實際課堂教學上的案例更是有待加強。 本研究採用準實驗研究設計,透過使用網路平台語言學習環境,提供以語料庫為基礎的語言輸入來探討台灣地區不同大學校院學生在英語語塊領域的學習與認知。本研究是根據資料驅動學習及發現式學習的方法將語料庫為基礎的語言輸入分成為重覆排列與隨機排列的方式,來呈現給學習者,學習者需完成線上提供的課程與練習(包含語言輸入),並完成學習任務。 研究設計分為二個實驗組與一個對照組,共計有182位學生參與,測量方式除問卷外尚包含了前測、後測與追蹤後測,主要的量化分析工具為多變量共變異分析、變異數分析、卡方檢定、與迴歸分析;質化資料的蒐集則來自於學生的線上課程討論。 主要的研究結果有三項,分別是: (一)、學生接受語料庫為基礎的語言輸入方式,無論排列方式是隨機或者是連續,都會在英語語塊的使用上優於對照組。並且使用連續排列方式的組別在克漏字的成績表現具有長期的效果。(二)、隨機排列的實驗組的表現證明了資料驅動學習的成效。尤其是在發現式學習的過程中,學生認出語塊結構的速度越來越快,並且在三種語塊的細部比較中,隨機組在翻譯測驗中都能維持穩定的進步。(三)、學生的英文閱讀與字彙程度與英語語塊學習並無明顯的關聯性。其他有關電腦使用及線上溝通等因素則僅有微量的影響。 總之,本研究結果建議將語料庫使用資料驅動學習及發現式學習溶入英語語塊學習,將會對英語為外語的教學帶來新的契機。 |
英文摘要 |
With the advances in computer technology, the importance of formulaic sequences has recently been recognized in the field of language learning and acquisition. In particular, the application of corpus studies on quantities of natural text has reinforced the theory that much of our language output is created in the form of multi-word chunks, stored and produced as such. However, experimental studies in exploring the use of formulaic sequences by second/foreign language learners still remain few and unrefined and little pedagogical progress has been made when it comes to the application in the ESL/EFL classroom. This quasi-experimental study intends to investigate the acquisition of formulaic expressions by 182 EFL students in Taiwan through applying a web-based EFL language learning platform (IWiLL), with corpus-based data used as input. Based on the concept of data-driven learning, the corpus-based input was designed into randomly-arranged and arranged patterns. Treatment procedures included an English proficiency test, a pretest, an immediate posttest and a delayed posttest on Cloze and Translation tests. MANCOVA, ANOVA, Chi-square, and Multiple Linear Regression tests were the major statistical techniques used in getting the quantitative data. Qualitative data were the self-reported reflective feedback collected from the discussion boards on the online learning platform. Specifically, this study addresses three issues: (a) Will learners consulting corpus-based input lead to better understanding and use of formulaic sequences? (b) Will learners who receive input in a randomly arranged pattern perform differently from those who receive input in an arranged pattern (c) What linguistic, affective and cognitive factors may or will facilitate the acquisition and the production of formulaic chunks? The results show that: (a) Exposure to corpus-based input treatment, both randomly-arranged and arranged ones, had significant impacts on learners' acquisition compared to those of the control group, immediately after the treatment and last for two months for Cloze test. (b) Processing corpus-based input through a data-driven learning task proved to be an efficient way of helping learners to internalize the target chunks. This has been validated by the results that learners under the randomly-arranged treatment constantly gain improvement on the translation test scores and spend decreasing time on the task. (c) The advantages of processing corpus-based input through the randomly-arranged treatment were more evident in the production of longer fixed lexical chunks, which suggests that learning through corpus data does not have to be arranged in a series and presented in a clear and neat concordancing patterns. (d) No significant association was found between English proficiency and the acquisition of formulaic sequences. Other variables like good control of computer literacy had small but positive effects on the acquisition of formulaic sequences through corpus-based input. The dissertation closes by exploring the implication of this study for discovery learning, and for teaching English formulaic sequences in EFL classroom settings. |
第三語言摘要 | |
論文目次 |
Acknowledgements i Chinese Abstract iv English Abstract v List of Tables x List of Figures xii CHAPTER 1 INTRODUCTION 1 1.1 Motivation and Significance of the Study 3 1.1.1 The Acquisition of Formulaic Sequences 3 1.1.2 The Application of Corpus-based Input and Data-driven Learning 7 1.1.3 Pedagogical Implication in EFL Context 9 1.2 Research Questions 11 1.3 Definitions of Terms 12 1.4 Scope of the Study 14 1.5 Organization of the Dissertation 16 CHAPTER 2 REVIEW OF THE LITERATURE 17 2.1 Lexical Approach and Pattern Grammar 17 2.2 The Nature and Use of Formulaic Sequences 20 2.2.1 Types of Lexical Items 20 2.2.2 Definitions of Formulaic Sequence (Chunks) 21 2.2.3 The Psycholinguistical Nature of Formulaic Sequences 22 2.2.4. The Function of Formulaic Language 26 2.3 The Acquisition Issue of Formulaic Sequences 29 2.3.1 Chunks and L1 Learning 29 2.3.2 Chunks and L2 Learning 29 2.3.3 The Difficulties of L2 Learners in Leaning Formulaic Sequences 31 2.4 Input Processing and Second Language Acquisition 32 2.4.1 Input-Output Promotes SLA 33 2.4.2 Computer-assisted Language Learning (CALL) and Language Input 35 2.5 Corpus Linguistics, Data-driven Learning and Language Teaching 36 CHAPTER 3 METHODOLOGY 39 3.1 Rationale for the Experimental Design 39 3.2 Research Questions and Hypotheses 42 3.3 Pilot Study 45 3.3.1 Design 45 3.3.2 Primary Outcome 47 3.3.3 The Modifications of the Pilot Study 48 3.4 Research Design 48 3.5 Participants 52 3.6. Selection of Target Formulaic Sequences 54 3.7. Instruments 57 3.7.1 Pretreatment Task 57 3.7.2 Pretest and Posttest 58 3.7.3 The Web-based Platform 60 3.7.4. Treatment 60 3.7.5 Self-reported Retrospective Learning Log 61 3.8. Treatment Procedure 62 3.9. Scoring and Analysis 65 3.9.1 Pattern-generating Task Scoring 65 3.9.2 The First Part of Pretest and Posttest: Cloze Test Scoring 66 3.9.3 Translation Test Scoring 66 3.10 Data Analysis 68 CHAPTER 4 FINDINGS OF THE STUDIES 70 4.1 Quantitative results 72 4.1.1 Descriptive Statistics 72 4.1.2. Prior Knowledge of the Target Forms among Groups 78 4.1.3 Effectiveness of the Corpus-based Input 81 4.1.4 Analysis of the Comparative Effects of Two Experimental Groups among Different Types of Chucks 86 4.1.5 Spending Time and Finding Chunk Patterns 96 4.1.6 Related Variables for the Formulaic Chunks Acquisition Scores 101 4.2 Qualitative results 116 4.2.1 Summary of Results 121 CHAPTER 5 DISCUSSION AND CONCLUSIONS 123 5.1 Discussion of the Findings 123 5.1.1. Immediate Effects of Corpus-based Input on the Use of Target Chunks 124 5.1.2 Application of DDL to Learners’ Processing of Formulaic Sequences 126 5.1.3 Cognitive Processes Engaged in Corpus-based Input 128 5.1.4 Different Effects on Different Types of Chunks 130 5.1.5 Reexamination of Web-based Language Learning Versus Individual Variation and Affective Factors 133 5.2. Pedagogical Implications 134 5.3. Limitations of the Study and Recommendations for Future Research 135 5.4 Conclusion 137 CHAPTER 6 REFERENCES 140 CHAPTER 7 APPENDICES 150 Appendix A 150 Appendix B 152 Appendix C 153 Appendix D 154 Appendix E 155 Appendix F 156 Appendix G 157 Appendix H 158 Appendix I 161 Appendix J 163 Appendix K 165 List of Tables Table 3.1 Six Target Chunks Used in the Piloting 46 Table 3.2 The Average Percentage of Correctness for Each Item 47 Table 3.3 Research Design Schema 49 Table 3.4 Demographic Overview for Total Subjects 54 Table 3.5 Selected Types of Lexical Bundles 56 Table 3. 6a Scoring Criteria for Type 1 Chunks in Translation Test 68 Table 3. 6b Scoring Criteria for Type 2 Chunks in Translation Test 68 Table 3. 6c Scoring Criteria for Type 3 Chunks in Translation Test 68 Table 4. 1 Descriptive Statistics of All the Baseline Variables for All Groups 74 Table 4. 2 The Summarized Results of the Self-reported Questionnaire 77 Table 4. 3 The PreTest results of Cloze and translation test 79 Table 4. 4 The Overall Results for the Cloze Test Scores 82 Table 4. 5 The Overall Results for the Translation Test Scores 82 Table 4. 6 ANVOA Results for Cloze Scores: Chunk Type 1 92 Table 4. 7 ANVOA Results for Cloze Scores: Chunk Type 2 92 Table 4. 8 ANVOA Results for Cloze Scores: Chunk Type 3 93 Table 4. 9 ANVOA Results for Translation Scores: Chunk Type 1 94 Table 4. 10 ANVOA Results for Translation Scores: Chunk Type 2 94 Table 4. 11 ANVOA Results for Translation Scores: Chunk Type 3 95 Table 4. 12 Results of Spending Time at Each Treatment for the Two Groups 97 Table 4. 13 The Results for Chunk Pattern Recognition in Each Treatment for the Two Groups 99 Table 4. 14a Relationship between English Proficiency and Pre-Post Test Improvement (Cloze) 102 Table 4. 14b Relationship between English Proficiency and Pre-Post Test Improvement (Translation) 102 Table 4. 14c Relationship between English Proficiency and Post- DPost Test Improvement (Cloze) 102 Table 4. 14d Relationship between English Proficiency and Post-DPost Test Improvement (Translation) 102 Table 4. 14e Relationship between English Proficiency and Pre-DPost Test Improvement (Cloze) 103 Table 4. 14f Relationship between English Proficiency and Pre-DPost Test Improvement (Translation) 103 Table 4. 15 Association between the Improved Pre-Post Scores and Important Related Variables for the Cloze Test (Cloze PreTest-PostTest). 107 Table 4. 16 Association between the Improved Pre-Post Score and Important Related Variables for the Translation Test (Translation PreTest-PostTest). 108 Table 4. 17 Association between the Mean Improved Post-Dpost Score and Important Related Variables for the Cloze Test. (Cloze PostTest-DPostTest) 109 Table 4. 18 Association between the Mean Improved Pre-Dpost Score and Important Related Variables for the Cloze Test (Cloze PreTest -DPostTest). 111 Table 4. 19 Association between the Mean Improved Pre-Dpost Score and Significantly Important Related Variables for the Translation Test. (Translation PreTest- DPostTest) 112 Table 4. 20 Intercorrelations between TOEIC and the Score of Each Test (overall) 113 Table 4. 21 Intercorrelations between TOEIC and the Score of Each Test Each Test (Randomly-arranged) 114 Table 4. 22 Intercorrelations between TOEIC and the Score of Each Test (Arranged) 114 Table 4. 23 Intercorrelations between TOEIC and the Score of Each Test (Control) 114 List of Figures Figure 3.1 Experimental Procedures 52 Figure 3.2 First page of the lesson 63 Figure 3. 3 the chunk lesson page 64 Figure 3.4 post-exposure task page 65 Figure 4.1. The mean scores on cloze test for three groups 83 Figure 4.2. The mean scores on translation test for three groups 87 Figure 4.3 The improved Post-Pre scores of each type of chunks on cloze test 93 Figure 4.4 The improved Post-Pre scores of each type of chunks on translation test 95 Figure 4.5 The chunk searching time on each treatment for the two groups 97 Figure 4.6 The chunk pattern recognition at each treatment for the two groups 99 |
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