Faculty Profiles – Computer Science
Irene Gassko
Lecturer, Donald Bren School of Information and Computer Sciences, UC Irvine
Irene Gassko currently teaches discrete math to computer science students and is looking for ways to help them master this way of thinking. Her interest in discrete math comes from middle and high school math clubs. She started to teach math to middle schoolers as a senior in high school and she always enjoyed infecting students with her enthusiasm for math. She has a Ph.D. in Computer Science from Boston University. Irene worked in industry and taught at BU, Northeastern University, and the Evergreen State College before coming to UCI. She has been a member of SIGCSE since her graduate studies and never lost interest in better teaching methods, even when she worked in industry.
On A Way to Specification Grading
Trying to help students and creating for them many opportunities to learn runs into the same problem every time: the students who need help don’t use it because their main motivation is grades and they skip everything that doesn’t bring points. We are trying to move to a kind of grading that will replace grade motivation with understanding motivation and various flavors of specification and mastery grading look promising. This quarter we don’t have enough support to go full swing, so we decided to implement some features and use tokens to encourage better study habits in students. I will share what we discover in the first 5 weeks of our spring quarter.
Navrati Saxena
Assistant Professor, Department of Computer Science, College of Science, San Jose State University
Prof. Saxena is an Assistant Professor at the Department of Computer Science, San Jose State University (SJSU), California, USA. Prior to joining SJSU, she worked as an Assistant/Associate Professor and director of the Mobile Ubiquitous System Information Center (MUSIC) at the College of Information and Communication Engineering (CICE), Sungkyunkwan University (SKKU), South Korea (http://lab.icc.skku.ac.kr/~navrati/index.html). At SKKU Prof. Saxena supervised 12 Ph.D. and 22 MS (research) students. She received her Ph.D. degree from the University of Trento, Italy. Her research interests involve 5G wireless, IoT, social networking, smart grids, Device to Device (D2D), vehicular communications, and the like. She has co-authored one book; filed two patents and published one international book chapter on 6G; more than 90 Intl. journals and many Intl. conferences, with more than 4,000 citations (Google Scholar). She also serves as a guest editor in different Intl. journals, Technical Program Committee (TPC) Chair/member in many Intl. conferences.
Implementing Specifications Grading System: My Journey So Far
I started participating in the TEA course from summer 2021 and since then I have implemented part of specifications grading and mastery learning in all my courses. In this short presentation I am going to showcase my journey so far implementing specifications grading systems in my courses and discuss my plans for their future implementations.
Mike Wu
Assistant Professor, Department of Computer Science, College of Science, San Jose State University
I am an assistant professor in the Department of Computer Science at San Jose State University. I teach several CS courses such as “Data Structures and Algorithms”, “Database Design and Implementation”, and “Big Data Using Machine Learning”.
Applying Specifications Grading to a Database Design and Project class and How It Matches to Course Learning Outcomes
In this workshop, I will present my work on the design and implementation of applying the Specifications Grading approach to my undergraduate course, “Database Designment and Implementation” in Fall 2021 semester and how it matches to the predefined course learning outcomes. The specifications grading used on homework assignments, exams, and particularly the course project will be demonstrated and explained. Comparing with the same course taught in the previous semester, this course used specifications grading approach has improved the grading clearness, minimized conflicts/arguments, reflected students’ learning outcomes, and motivated students to learn. .