报告人：朱哲民
报告地点：腾讯会议ID:867-306-813
报告时间：2022年12月16日星期五9:00-10:00
报告摘要:
Cognitive diagnosis models have become popular in educational assessment and are used to provide more individualized feedback about a student’s specific strengths and weaknesses than traditional total scores. However, if the testing data are contaminated by certain biases or aberrant response patterns, such predictions may not be accurate. The current research objective is to develop a new person-fit method that is based on machine learning and improves the functionality of existing person-fit methods. Various simulations were designed under three aberrant conditions: cheating, sleeping and random guessing. Simulation results showed that the new method was more powerful and effective than previous methods, especially for short-length tests.
主讲人简介：
朱哲民，统计学博士，硕士生导师，北华大学教育科学学院副教授，普渡大学访问学者，在British Journal of Mathematical and Statistical Psychology、Applied Psychological Measurement、Frontiers in Psychology发表SSCI论文3篇，在《中国考试》等国内刊物发表多篇CSSCI论文，主持国家社科基金青年项目1项，省级项目多项，获省级政府奖1项。主要研究领域为教育测量与评价、教育统计方法，当下专注于aberrant response，IRT model，learning trajectories。
