报告人：Ni Bei
报告地点：腾讯会议ID:145 777 124
报告时间：2021年11月18日星期四10:00-11:00
报告摘要：
Mathematical word problems have long drawn educational researchers’ attention. Through word problems, students have the opportunity to apply and demonstrate their mathematical thinking to real-life problem-solving. However, not all math problems are created equally: there are many potential factors, including item structure, number properties, and linguistic features within a word problem that can negatively affect students’ responses, irrespective of their true mathematical understanding. This study proposes network analysis modeling as a novel approach for quantifying item process data at student, item, and student-item hybrid levels. We demonstrate these methods using 30 elementary students’ action sequences (eight per item) on two comparable math items that differed primarily in type of verbal information presented.
主讲人简介：
Ni Bei, Ph.D. candidate in Measurement & Statistics at the University of Washington’s College of Education, and she obtained her master’s degree in a similar program from the University of Illinois at Urbana-Champaign. Her research interest is broadly in using statistical models (e.g., social network analysis, multilevel modeling) to analyze real-time student responses from online exams to better understand student thinking.
