PerformanceVis:Visual analytics of student performance data from an introductory chemistry course  

作　　者：Haozhang Deng Xuemeng Wang Zhiyi Guo Ashley Decker Xiaojing Duan Chaoli Wang G.Alex Ambrose Kevin Abbott 

机构地区：[1]University of Rochester,Rochester,NY 14627,United States [2]Fudan University,Shanghai 200433,China [3]University of Notre Dame,Notre Dame,IN 46556,United States

出　　处：《Visual Informatics》2019年第4期166-176,共11页可视信息学（英文）

基　　金：the U.S.National Science Foundation through grants IIS-1455886 and DUE-1833129;the Schlindwein Family Tel Aviv University-Notre Dame Research Collaboration,United States Grant.Haozhang Deng,Xuemeng Wang,Zhiyi Guo,and Ashley Decker conducted this work as an undergraduate research project at the University of Notre Dame during Summer 2019.

摘　　要：We present PerformanceVis,a visual analytics tool for analyzing student admission and course performance data and investigating homework and exam question design.Targeting a university-wide introductory chemistry course with nearly 1000 student enrollment,we consider the requirements and needs of students,instructors,and administrators in the design of PerformanceVis.We study the correlation between question items from assignments and exams,employ machine learning techniques for student grade prediction,and develop an interface for interactive exploration of student course performance data.PerformanceVis includes four main views(overall exam grade pathway,detailed exam grade pathway,detailed exam item analysis,and overall exam&homework analysis)which are dynamically linked together for user interaction and exploration.We demonstrate the effectiveness of PerformanceVis through case studies along with an ad-hoc expert evaluation.Finally,we conclude this work by pointing out future work in this direction of learning analytics research.

关 键 词：Student performance Item analysis Grade prediction Learning analytics Knowledge discovery 

分 类 号：H31[语言文字—英语]