A Study On Foreign Student’s Acquisition Of Chinese Unmarked Passive Sentences

Passive sentences in modern Chinese could be divided into two categories: one was marked passive sentences with marks such as "被","叫","让",etc.,which were represented by "被" sentences;and the other one was unmarked passive sentences without marks mentioned above.Compared with marked passive sentences,unmarked passive sentences appeared more frequently in spoken language,in which foreign students often made mistakes while studying.But currently,in the field of teaching Chinese as a foreign language(TCFL)and second language acquisition,there were more researches on marked passive sentences,in particular with "被" sentences,than those specifically focusing unmarked passive sentence which had been rare up to now.Searching from HSK Dynamic Composition Corpus and Global Chinese Interlanguage Corpus to obtain students’ bias error corpus on unmarked passive sentences,this thesis adopted Error Analysis Theory to classify and analyze these errors,aiming to help TCFL teachers predicting student’s possible errors in their learning of unmarked passive sentences,formulating teaching strategies,and improving teaching efficiency.This thesis was mainly divided into the following five chapters.Chapter One defined unmarked passive sentences from the perspective of research on Chinese as a second language,clarified research scope,and comparatively analyzed unmarked and marked passive sentences to find out the differences in their usage conditions.Chapter Two introduced the process and result of searching from HSK Dynamic Composition Corpus and Global Chinese Interlanguage Corpus,and sorted out collected error corpus.Chapter Three classify the collected error corpus,summarized foreign students’ main error types in their process of their learning unmarked passive sentences.Chapter Four analyzed relative reasons in the process of their learning unmarked passive sentences according to the error types of Chapter Three.Chapter Five offered suggestions for teaching unmarked passive sentences based on the results of the research and analysis of foreign students’ error corpus.