我校語言教育與測評研究中心（CLEAR）于2021年4月16日下午舉辦本年度第一次研究方法工作坊，主題為“多側面Rasch測量模型（MFRM)在語言能力評價中的應用”。工作坊由國際商務外語學院2019級語言學班研究生宋凱月主講，CLEAR中心主任蔡雨陽教授協(xié)助講解。外語學院部分教師及研究生參加了此次工作坊。
The Centre for Language Education and Assessment Research (CLEAR) launched the 2021 workshop series on 16th April, with a workshop on the use of Many-Facet Rasch Measurement (MFRM). The workshop was co-instructed by Ms Kaiyue Song (a graduate student of Applied Linguistics) and Professor Yuyang Cai (Director of CLEAR).
工作坊伊始，蔡雨陽教授介紹多側面Rasch模型（MFRM）在外語教學和測評中的用途，指出MFRM模型可以較好控制評分員主觀性差異、評分標準質(zhì)量、量表屬性等因素帶來的測量偏差，從而提供較為公平的分數(shù)。
Professor Cai opened the workshop by introducing the benefits of using MFRM to control for bias form raters, rubrics, and the scale in perform-based assessment in the context of foreign language education and assessment. Next, Ms. Song briefly covered the mathematics underlying MFRM before moving to a review of empirical studies using MFRM in language research.
接著由宋凱月同學簡要介紹了MFRM的數(shù)學機制以及在外語研究中運用文獻。隨后以2名評分員對120份作文的雙評分數(shù)數(shù)據(jù)為例，逐步展示了如何操作Facet軟件進行MFRM分析。宋凱悅最后強調(diào)了教學過程中教師可以運用MFRM來確定極端分值并重新賦分，以最大程度保證分數(shù)結果的公正性。
In the following 60 minutes, Ms Song demonstrated step-by-step how to use the program FACET to conduct MFRM with a set of rating data by two raters. She closed her demonstration by emphasizing that MFRM can be a powerful and efficient tool for detecting problematic score assignment by human raters to enhance scoring validity.
最后，由蔡雨陽教授作了總結發(fā)言，對宋凱月同學的精彩講解表示感謝，并鼓勵老師和同學們充分運用MFRM的功能提升外語教學和科研質(zhì)量。師生們對此次工作坊表現(xiàn)出濃厚的興趣，會后參會者仍然同主講人就如何運用MFRM模型進行科研合作進行了深刻的探討。
After active discussion between the instructors and the audience, Professor Cai closed the workshop with a high appreciation of Ms Song’s excellent instruction and a call for use of MFRM in foreign language teaching and research.