Chapter One Introduction
1.1 Background of the Study
China, like many other countries, has been advocating the application oftechnology in language learning, especially in second or foreign language acquisitionfor a long time. A large number of published government documents have emphasizedthe importance of applying technology in learning English as a foreign language (EFL).College English Curriculum Requirements states clearly the requirements of massiveuse of advanced information technology in the designing of computer-based and/orweb-based courses (Ministry of Education, 2007). The nineteenth chapter of theNational Medium- and Long-term Plan for Education Reform and Development(2010-2020) also requires the accelerating of educational informationization process(The Central People's Government of the People's Republic of China, 2010). Inaddition，the National English Curriculum Standards for Primary School suggests theutilization of modern educational technology to help elementary school studentsbroaden learning sources and to strengthen their ability to use the target language (N/A,2012). In fact，researchers in the United States began to integrate computers with languagelearning early in the 1960's (Marty，1981). This practice, labeled as computer-assistedlanguage learning (CALL), was introduced into China in the 1970，s (Jia，2007)] CALLis briefly defined as 'the search for and study of applications of the computer inlanguage teaching and learning，，（Levy，1997; 1). It embraces a wide range ofinformation technology applications and approaches to teaching and learning foreignlanguages, from the 'traditionar drill-and-practice programs that characterize CALL tomore recent manifestations of CALL，such as those used in a virtual learningenvironment md/or web-based distance learning.
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1.2 Purpose of the Study
With the successive publication of research on technology-enhanced Englishvocabulary learning, the crucial role of technology in English vocabulary learning hasbeen repeatedly acknowledged, which results in a large body of literature that strives tolook into its effectiveness (e.g., see Perez & Desmet, 2012; Song, 2008). No consensus,however, has been reached yet.Many literature reviews have tried to analyze the discrepant research findings ofeffectiveness and reached their conclusions (e.g.，see Choo，Lin, & Pandian，2012;Mohsen & Balakumarohsetij 2011). Nonetheless, most of the existing reviews arenarrative reviews that may easily plunge into unscientificity because they treat everyresearch result equally and draw conclusions based on the amount of the results (see amore detailed discussion of the disadvantages of narrative reviews in section 3.2.2).Hence, the current study intends to adopt a more scientific method (i.e., quantitativesystematic review, also known as meta-analysis) to synthesize previous quantitativeresearch results and to quantify their overall effectiveness of technology on Englishvocabulary learning further.
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Chapter TwoLiterature Review
2.1 Effectiveness in Technology-Enhanced English Vocabulary Learning
In spite of an increasing growth of interest and emerging literature intechnology-enhanced English vocabulary learning, it is surprising to find that researchhas revealed inconsistent results, showing a wide variation in the effectiveness oftechnology on English vocabulary learning.The first inconsistency lies in the general effectiveness that technology has onEnglish vocabulary learning. Some research results demonstrate positive effect (e.g.,see Maftoon, Hamidi, & Sarem, 2012; Yu, 2009; Zhang & Chen, 2011). Others doubtthat the effectiveness may even be less than traditional method's (e.g., see De la Fuente,2003;Koren，1999).Much research supports the proposition that technology influences vocabularylearning positively.Yu (2009) conducts a data-driven learning (DDL) experiment with college students,choosing verbs as target words. After a 12-week experiment, the post-test mean score ofthe experiment group rises to 87.08, which is higher than the pre-test mean score of69.12. This score also exceeds that of the control group that adopts the traditional wayof vocabulary teaching, reaching a significant difference.
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2.2 Meta-Analyses of Technology-Enhanced English Vocabulary Learning
A large amount of researchers have made efforts to perform meta-analyses ontechnology-enhanced English vocabulary learning from different perspectives (e.g., seeAbraham, 2008; Chiu, 2013; Perez, Noortgate, & Desmet，2013; Yun，2011),Abraham (2008) analyzes 11 pieces of research on computer-assisted incidentalvocabulary learning. The results are that computer-mediated glosses have a large effecton incidental vocabulary learning: in immediate vocabulary post-test, d = 1.40; indelayed vocabulary post-test, d = 1.25. These effect sizes vary due to level ofinstruction (beginning, intermediate, advanced), text type (e^qpository，narrative), andassessment (productive，receptive). Glosses are most effective for intermediate-levellearner: in immediate vocabulary post-test，d= 1.61; in delayed vocabulary post-test, d=2.06. Similarly larger effect sizes are found in expository test: in immediatevocabulary post-test, d = 1.52; in delayed vocabulary post-test, d = 1.34. Moreover,glosses have a larger effect on learners' performance on immediate receptivevocabulary tests (d = 1.81) than on productive assessments (d = 0.60). In delayedpost-tests，receptive post-tests (d= 1.43) are also larger than productive tests、d 二 0.21).One concern, however, for the results is that the relatively small number of studies foreach category contributes to the non-significant differences found in level of instructionand text type.
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Chapter Three Research Method.......... 21
3.1 Research Questions......... 21
3.2 Meta-Analysis......... 22
3.3 Collection and Selection of Primary Research......... 31
3.4 Descriptions of Coding Characteristics .........37
3.5 Summary 38Chapter Four Results and Discussion......... 40
4.1 Descriptive Statistics......... 40
4.2 Results .........46
4.3 Discussion......... 52
4.4 Summary ......... 61
Chapter Five Conclusion .........62
5.1 Major Findings......... 62
5.2 Limitations of the Study......... 64
5.3 Implications and Suggestions for Future Research......... 65
Chapter Four Results and Discussion
4.1 Descriptive Statistics
Approximately 3,400 journal papers, meeting reports, masters theses and doctoraldissertations completed between Jan. 1st，2004 and Dec. 31th，2013 are gatheredthrough the first filtering process by keyword searching such as technology，vocabulary learning, and computer (see Table 3.2). After applying the inclusioncriteria in the second filtering process, 93 pieces of literature are included. Then, onemore restrict inclusion criterion is used to fbrther filter the collected 93 pieces ofliterature. Through the final filtering process，13 studies (20 effect sizes)，whichsurvive the three-filtering process，are selected as primary research and applied toextract effect size statistics (see Appendix 1 for primary research included). Detailedinformation and data of primary research and effect sizes are presented in Table 4.1
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Conclusion
As the last chapter，this chapter serves as a brief conclusion of the whole thesis.There are three sections in this chapter. All the results of this meta-analysis aresummarized in the first section. Then in the second section, limitations to this studyare discussed from aspects of sample size, computation method, and resultspresentation. The last section offers some implications and suggestions for fiitureresearch on technology-enhanced English vocabulary learning and futuremeta-analyses on this topic respectively. The goal of this study is two-fold: quantify the extent of the overall effect thattechnology has on English vocabulary learning，and examine influences of twomoderator variables (learning method and test type) on the overall effect.To answer the questions，meta-analysis is adopted in this study. Research objects(primary research) are collected in four ways: search electronic databases, searchjournals by hand, forward search and backward search. The collected primaryresearch is then filtered through a set of inclusion criteria. Finally，thirteen pieces ofprimary research are screened out for meta-analysis, from which twenty effect sizesand other information are extracted according to pre-set coding principles. Theextracted data are computed with aid of professional meta-analysis softwareComprehensive Meta-Analysis. Descriptive statistics and the results of the researchquestions are summarized and analyzed later. Through the analysis of the final results,several major findings are found.
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