基于句酷批改網(wǎng)的寫作文本句法特征探究
發(fā)布時間:2021-08-26 22:41
本文探究了句酷批改網(wǎng)對中國大學(xué)生英語作文句法復(fù)雜度的內(nèi)在評價機制。文章首先回顧自動化寫作評分系統(tǒng)的相關(guān)文獻,如對國外研發(fā)的PEG,IEA和E-rater系統(tǒng)的研究。與上述自動評分系統(tǒng)相比,國內(nèi)設(shè)計研發(fā)的句酷批改網(wǎng)自動寫作評分系統(tǒng)仍有不足和亟需改進。句酷批改網(wǎng)自動評分系統(tǒng)在句法復(fù)雜度和句法結(jié)構(gòu)層面的反饋功能仍存在缺陷,相關(guān)研究也較為不足,句酷批改網(wǎng)句法層面上的內(nèi)部評價機制“黑箱”狀態(tài)仍有待探究。本研究基于該研究領(lǐng)域的空缺,對句酷批改網(wǎng)自動評分系統(tǒng)在句法復(fù)雜度層面上的評分機制進行探究,旨在為未來的英語教、學(xué)和自動評分系統(tǒng)改進提供參考。在本研究中,我們從句酷批改網(wǎng)上抽取2300篇學(xué)生英語寫作文本(學(xué)生對象主要為重慶大學(xué)非英語專業(yè)大學(xué)二年級學(xué)生,同時文本附有相應(yīng)的句酷批改網(wǎng)成績),并使用由陸小飛博士所研發(fā)的句法復(fù)雜度分析器(Second Language Syntactic Complexity Analyzer,L2SCA)、SPSS統(tǒng)計軟件包和R Studio算法包(隨機森林和邏輯回歸的分類算法)來分析學(xué)生寫作文本總體句法復(fù)雜度情況和高分組學(xué)生寫作文本中的重要句法特征,以了解學(xué)生在作文中使...
【文章來源】:重慶大學(xué)重慶市 211工程院校 985工程院校 教育部直屬院校
【文章頁數(shù)】:45 頁
【學(xué)位級別】:碩士
【文章目錄】:
ABSTRACT
中文摘要
ABBREVIATIONS
CHAPTER ONE INTRODUCTION
1.1 RATIONALE OF THE STUDY
1.2 SIGNIFICANCE OF THE STUDY
1.3 ORGANIZATION OF THE THESIS
CHAPTER TWO LITERATURE REVIEW
2.1 AUTOMATIC WRITING EVALUATION
2.2 SECOND LANGUAGE SYNTACTIC COMPLEXITY AND L2SCA
2.3 MACHINE LEARNING ALGORITHMS FOR DATA MINING BASED ON R
2.4 SUMMARY
CHAPTER THREE METHODOLOGY
3.1 RESEARCH QUESTIONS
3.2 THE DATASET
3.3 PROCEDURES OF DATA ANALYSIS
3.4 SUMMARY
CHAPTER FOUR RESULTS AND DISCUSSION
4.1 OVERALL WRITING PERFORMANCE ON SYNTACTIC COMPLEXITY
4.2 ACCURACY AND FITNESS EVALUATION OF MODELS
4.3 WRITING TEXT SYNTACTIC FEATURES SELECTION
4.4 FURTHER DISCUSSION OF THE PRESENT STUDY
CHAPTER FIVE CONCLUSION
5.1 MAJOR FINDINGS
5.2 IMPLICATIONS FOR LANGUAGE EDUCATION AND RESEARCH IN CHINA
5.3 LIMITATIONS AND FUTURE RESEARCH
REFERENCES
APPENDIX
ACKNOWLEDGEMENTS
本文編號:3365103
【文章來源】:重慶大學(xué)重慶市 211工程院校 985工程院校 教育部直屬院校
【文章頁數(shù)】:45 頁
【學(xué)位級別】:碩士
【文章目錄】:
ABSTRACT
中文摘要
ABBREVIATIONS
CHAPTER ONE INTRODUCTION
1.1 RATIONALE OF THE STUDY
1.2 SIGNIFICANCE OF THE STUDY
1.3 ORGANIZATION OF THE THESIS
CHAPTER TWO LITERATURE REVIEW
2.1 AUTOMATIC WRITING EVALUATION
2.2 SECOND LANGUAGE SYNTACTIC COMPLEXITY AND L2SCA
2.3 MACHINE LEARNING ALGORITHMS FOR DATA MINING BASED ON R
2.4 SUMMARY
CHAPTER THREE METHODOLOGY
3.1 RESEARCH QUESTIONS
3.2 THE DATASET
3.3 PROCEDURES OF DATA ANALYSIS
3.4 SUMMARY
CHAPTER FOUR RESULTS AND DISCUSSION
4.1 OVERALL WRITING PERFORMANCE ON SYNTACTIC COMPLEXITY
4.2 ACCURACY AND FITNESS EVALUATION OF MODELS
4.3 WRITING TEXT SYNTACTIC FEATURES SELECTION
4.4 FURTHER DISCUSSION OF THE PRESENT STUDY
CHAPTER FIVE CONCLUSION
5.1 MAJOR FINDINGS
5.2 IMPLICATIONS FOR LANGUAGE EDUCATION AND RESEARCH IN CHINA
5.3 LIMITATIONS AND FUTURE RESEARCH
REFERENCES
APPENDIX
ACKNOWLEDGEMENTS
本文編號:3365103
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