Keynotes/Invited Lectures


What Can we Learn from Student Course-Grade Data?
Prof. Gary Weiss
Professor, Computer & Information Science Department, Fordham University, USA
Abstract:
Data science has made great inroads into many areas of our daily life, plays a key role in many industries, and is beginning to play an increasing role in education. In this talk I will describe the variety of knowledge that can be gleaned by analyzing undergraduate course-grade data (i.e., data that list all courses a student took and their corresponding grade). I will show how this data can be used to: identify strong and weak instructors and easy and hard graders; characterize grading patterns at the student, course, instructor, and department level; characterize how students sequence their courses and the impact of this sequencing on student learning; identify majors in which a student will perform well; and group courses based on student course co- enrollments or similar patterns in student performance.
Speaker’s Biography:
Dr. Gary M. Weiss is a Professor of Computer and Information Science at Fordham University. He received his B.S. degree in Computer Science from Cornell University, M.S. degree in Computer Science from Stanford University, and doctorate in Computer Science from Rutgers University. He joined Fordham in 2004 after working for nearly two decades at AT&T Bell Labs. His main research area is data mining and machine learning. Since 2019 he has been working with two of his colleagues in the area of educational data mining, with the goal of improving higher education with a data-intensive approach. Dr. Weiss has published over ninety papers and book chapters in the area of data science. He is currently Co-PI on a $4M US National Science Foundation scholarship grant to educate the next generation of cybersecurity practitioners by incorporating data science into the cybersecurity curriculum and is the director of Fordham’s MS in Computer Science program.



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