不妨设想这样一个场景，面对超过两千名学生，为同一门课程的组织115个小班，如何确保小班之间教学内容和评价的一致性？如何找出那些在困境中挣扎的学生并提供帮助？下一代数字教学环境（Next Generationl Digital Learning Environmen...不妨设想这样一个场景，面对超过两千名学生，为同一门课程的组织115个小班，如何确保小班之间教学内容和评价的一致性？如何找出那些在困境中挣扎的学生并提供帮助？下一代数字教学环境（Next Generationl Digital Learning Environment，NGDLE）正是为了解决这些问题而付诸实施的。展开更多
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 cour...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.展开更多
文摘不妨设想这样一个场景，面对超过两千名学生，为同一门课程的组织115个小班，如何确保小班之间教学内容和评价的一致性？如何找出那些在困境中挣扎的学生并提供帮助？下一代数字教学环境（Next Generationl Digital Learning Environment，NGDLE）正是为了解决这些问题而付诸实施的。
基金the U.S.National Science Foundation through grants IIS-1455886 and DUE-1833129the 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.
文摘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.
