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。

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.

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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