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The Effects Of Textual Enhancement And Rule Presentation On Chinese Learners’ System Learning Of English Dative Alternation

Posted on:2014-07-02Degree:MasterType:Thesis
Country:ChinaCandidate:P P YangFull Text:PDF
GTID:2267330401484165Subject:Foreign Linguistics and Applied Linguistics
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In the field of second language acquisition and foreign language instruction,researchers are all striving to explore the methods and approaches of promoting L2learners’ language proficiency. Input is the necessary condition of learning foreignlanguages, and noticing a form is the antecedent condition for language acquisition.Only the noticed input can be processed further and possibly acquired by L2learners.In the meaning based context, whether there is efficacy for the learning of grammarforms by attracting learners’ attention to target structures through input enhancementinstructional techniques remains a focus of the foreign language instruction. And L2acquisition involves two kinds of different but consecutive learning modes: itemlearning versus system learning. To learn relatively complicated linguistic features,learners have difficulties in acquiring their systematic knowledge. To date, mostrelevant studies are to investigate the effects of different instructional techniques onindividual items. This study attempts to explore the effects of implicit textualenhancement technique and explicit rule presentation technique on system learning ofEnglish dative alternation. The aim of the study is to provide robust evidences forselecting efficient instructional techniques in foreign language teaching and learning.The subjects are from three parallel classes in Grade1of Laiwu NO.2HighMiddle School in Shandong.90participants are in three groups: one is the textualenhancement group (the TE group), one is the rule presentation group (the RP group),and the third one is the TE&RP group. The TE group receives the typographicallyenhanced textual input, the RP group receives one grammatical rule sheet and theunenhanced textual input, and the TE&RP group receives the grammatical rule sheetand the typographically enhanced textual input. Following each text of the textualinput, some comprehending questions need to be answered by all the three groups. There are three tests including pretest, posttest and delayed posttest in theexperiment. The data are analyzed by the software SPSS16.0. The pretest resultsshow that the three groups differ insignificantly in their knowledge of English dativeverbs. The results of the posttest and the delayed posttest indicate the positive effectsof the three input enhancement techniques on item learning of English dative verbs.However, for its system learning, the effects on the TE group are limited, the effectson the RP groups are better but not the best, while the effects on the TE&RP group arethe best and most significant. It implies that just noticing the target forms can notguarantee the acquisition of their systematic rules. The scores of the three groups alldecrease from the posttest to the delayed posttest, whereas the third group still is thebest. It supports that, to improve L2learners’ system learning of relativelycomplicated grammar structures, it needs not only to facilitate L2learners to noticethe individual items, but also to promote their generalization and understanding of thesystematic rules of the target structure.Some theoretical and pedagogical implications can be obtained from the presentstudy. It proves that noticing at the level of understanding, namely abstracting theunderlying rules is beneficial for L2acquisition. In language teaching, ChineseEnglish teachers can employ the combinative approach of different implicit andexplicit input enhancement instructional techniques to promote L2learners’ systemlearning of the relatively complicated grammar structures. And textbook writers couldget some inspiration from the study as well. At last, the limitations of this study andsuggestions for further researches are provided.
Keywords/Search Tags:noticing, textual enhancement, rule presentation, English dative alternation, system learning
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