计算机软件新技术国家重点实验室(南京大学)

南京大学计算机科学与技术系

南京大学软件学院

南京大学人工智能学院

摘要

    Due to the limitation of public school systems, many students pursue private supplementary tutoring for improving their academic performance. Different from public schools, the private online education provides diverse courses and satisfy differentiated demands of the students. Students’ behavior and performance in online supplementary learning are relevant to not only personal attributes, but also some factors such as city levels, grades and family situation. Existing studies mostly rely on panel survey/questionnaire data and few studied online private tutoring. In this paper, with 11,392 anonymous K-12 students’ 3-year learning data from one of the world’s largest online extra-curricular education platforms, we uncover students’ online learning behaviors and infer the impact of students’ home location, family socioeconomic situation and attended school’s reputation/rank on the students’ private tutoring course participation and learning outcomes. Further analysis suggests that such impact may be largely attributed to the inequality of access to educational resources in different cities and the inequality in family socioeconomic status. Finally, we study the predictability of students’ performance and behaviors using machine learning algorithms with different groups of features, showing students’ online learning performance can be predicted with MAE< 10%.

报告人简介：

   Xiaoming Fu is a Chair Professor of Computer Science at the Institute of Computer Science, University of Goettingen and leading the Computer Networks (NET) Research Group. He is a fellow of IET and Academia Europaea, a senior member and distinguished lecturer of IEEE, a member of ACM and a member of GI.

He received his Ph.D. from Tsinghua University in 2000. He was then a research staff at TU Berlin before joining the faculty at the University of Göttingen in 2002, where he has been a professor and head of computer networks group since 2007. His research interests lie in networked systems and applications, including mobile and cloud computing, social networks and big data analysis.

傅晓明  教授

欧洲科学院院士、IEEE杰出讲师、英国工程技术学会会士