我校语言教育与测评研究中心（CLEAR）于2021年4月16日下午举办本年度第一次研究方法工作坊，主题为“多侧面Rasch测量模型（MFRM)在语言能力评价中的应用”。工作坊由国际商务外语学院2019级语言学班研究生宋凯月主讲，CLEAR中心主任蔡雨阳教授协助讲解。外语学院部分教师及研究生参加了此次工作坊。
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，蔡雨阳教授介绍多侧面Rasch模型（MFRM）在外语教学和测评中的用途，指出MFRM模型可以较好控制评分员主观性差异、评分标准质量、量表属性等因素带来的测量偏差，从而提供较为公平的分数。
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.
接着由宋凯月同学简要介绍了MFRM的数学机制以及在外语研究中运用文献。随后以2名评分员对120份作文的双评分数数据为例，逐步展示了如何操作Facet软件进行MFRM分析。宋凯悦最后强调了教学过程中教师可以运用MFRM来确定极端分值并重新赋分，以最大程度保证分数结果的公正性。
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，由蔡雨阳教授作了总结发言，对宋凯月同学的精彩讲解表示感谢，并鼓励老师和同学们充分运用MFRM的功能提升外语教学和科研质量。师生们对此次工作坊表现出浓厚的兴趣，会后参会者仍然同主讲人就如何运用MFRM模型进行科研合作进行了深刻的探讨。
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.