Editors
- Yi Zheng 郑 屹, Arizona State University
- Hua-Hua Chang 张华华, Purdue University
- Tao Xin 辛 涛, Beijing Normal University 北京师范大学
The Chinese/English Journal of Educational Measurement and Evaluation | 教育测量与评估双语期刊 is published in Chinese and English and is cosponsored by National Council on Measurement in Education (NCME) and Beijing Normal University (BNU). The Chinese/English Journal of Educational Measurement and Evaluation | 教育测量与评估双语期刊 aims to publish original empirical articles which present new approaches to educational measurement and evaluation, as well as review articles that share advances in scholarship and practice between the United States, China, and the assessment and evaluation communities throughout the world.
《教育测量与评估双语期刊》(CEJEME)由美国教育测量协会(NCME)和北京师范大学共同承办, 并将以两种语言(中文和英文)出版。CEJEME将发表关于教育测量与评估新方法的原创性实证文章,同时发表能够促进中国与美国及其他地区教育测量和评估团体间学术和实践经验的交流的评论性文章。
Current Issue: Volume 6, Issue 1 (2025) Special Issue on Artificial Intelligence and Machine Learning in Educational Measurement (Part 2)
Introduction
This issue is the second part of CEJEME's Special Issue on Artificial Intelligence and Machine Learning in Educational Measurement. Building on the foundational discussions in Part 1, this installment further explores the evolving role of AI and ML in assessment, evaluation, and learning analytics. The four articles in this issue examine a broad spectrum of topics, including a survey on the use of ML in the measurement community, an investigation into the effectiveness of digital tools in math assessments, an analysis of complex log data using advanced clustering techniques, and a study on mitigating bias in AI-driven assessments. These contributions provide deeper insights into the methodological advancements and ethical considerations necessary for integrating AI and ML into educational measurement. As the field continues to evolve, this special issue underscores the need for open conversations and collaborations among measurement professionals to ensure that ML/AI-powered assessments are not only technologically sophisticated but also equitable, transparent, accountable, and truly supportive of diverse learners. A final installment of this special issue will follow in the coming months. (注:中文版本将稍后上线。感谢您的耐心等待!) -- Yi Zheng and Okan Bulut, co-editors of the special issue.Articles
Editorial: Special Issue on Artificial Intelligence and Machine Learning in Educational Measurement (Part 2)
Okan Bulut and Yi Zheng
编者按:《教育测量中的人工智能与机器学习》专题特刊(第二部分)
Okan Bulut and Yi Zheng
MxML (Exploring the Relationship between Measurement and Machine Learning): Survey of the Measurement Community
Yi Zheng, Sijia Huang, Steven Nydick, and Susu Zhang
MxML (探索测量与机器学习的关系): 对测量学界的问卷调查
Yi Zheng, Sijia Huang, Steven Nydick, Susu Zhang, and Xiaoran Wang
Toward Better Digital Tool Designs to Assist Students on Math Assessments
Hongwen Guo, Matthew S. Johnson, Luis Saldivia, and Michelle Worthington
探求更好的数字工具设计来帮助参加数学评估的学生
Hongwen Guo, Matthew S. Johnson, Luis Saldivia, and Michelle Worthington
Uncovering Dimensions of Learning Management System Event Logs: Insights from Exploratory Factor Analysis, Psychometric Network Analysis, and Consensus Hierarchical Clustering
Surina He, Okan Bulut, Ying Cui, and Tarid Wongvorachan
解析学习管理系统事件日志的维度:来自探索性因素分析、心理测量网络分析和共识层次聚类的方法的启示
Surina He, Okan Bulut, Ying Cui, and Tarid Wongvorachan
The Human Complimentary Usage of AI and ML for Fair and Unbiased Educational Assessments
Hayri Eren Suna and Mahmut Özer
人工智能与机器学习在人类辅助下的公平教育评估应用
Hayri Eren Suna and Mahmut Özer