淡江大學覺生紀念圖書館 (TKU Library)

系統識別號 U0002-2202200718464200
中文論文名稱 語言輸入對台灣英語學習者英語語塊習得的影響
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 95
學期 1
出版年 96
研究生中文姓名 陳惠如
研究生英文姓名 Hui-Ju Chen
學號 890010043
學位類別 博士
語文別 英文
口試日期 2007-01-22
論文頁數 165頁
口試委員 指導教授-衛友賢
中文關鍵字 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 
英文關鍵字 formulaic sequences  language input  data-driven learning  discovery learning  second language acquisition 
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。
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。
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.
論文目次 Acknowledgements i
Chinese Abstract iv
English Abstract v
List of Tables x
List of Figures xii
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
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
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
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
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
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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