首页> 中文学位 >用多层面Rasch模型分析做事测试的分数差异——对大学英语四、六级口语考试(CET-SET)的一次探索
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用多层面Rasch模型分析做事测试的分数差异——对大学英语四、六级口语考试(CET-SET)的一次探索

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目录

英文文摘

Acknowledgements

Chapter 1 Introduction

Chapter 2 Performance Testing

Chapter 3 Issues in testing Second Language Speaking

Chapter 4 Many-Facet Rasch Model (MFRM)

Chapter 5 The Present Study

References

Appendix I CET-SET Rating Scale

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摘要

As a comparatively direct measure of learners' communicative language ability,performance test (typically writing and speaking test) claims construct validity and a strong power for predictive utility of test scores and is gaining increasing interest both in practice and research in language testing. Despite the seemingly direct relationship between the underlying constructs and scores, a routine but more complicated procedure should still be followed in test validation. The present thesis aims to give a general overview of justifications for and problems involved in test validation in performance testing context and illustrate how Many-facet Rasch Model (MFRM)could be adopted as an effective tool in addressing problems in test validation.As an illustration, the present study investigates and models possible sources of score variability within the framework of MFRM, based upon raw scores of CET-SET (College English Test-Spoken English Test) administered on 19th and 20th, Nov. 2005in Hangzhou. The results demonstrate that CET-SET could successfully separate candidates into statistically distinct levels of proficiency and facets in the model such as rater, task, rating domains and rating scales generally function well within certain limits. However, tendency to give central ratings along the scale, less variability across three rating domains and certain patterns of bias in raters' rating pattern are detected. Fair scores for each candidate are also provided, minimizing the variability due to facets other than candidates' ability.MFRM manifests itself as effective in detecting whether each test method facet functions as intended in such performance test setting and providing useful feedback for test improvement.

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