2023 ASEE Annual Conference & Exposition

Identifying Student Profiles Related to Success in Discrete Math CS Courses

Presented at COED: All about That Math

Researchers in Computer Science education are working to identify sets of student features that play a role in course performance with the goal of helping students improve their outcomes. Specifically, researchers identified motivation, certain study behaviors, sense of belonging, and growth mindset as important factors. This prior work mainly focused on introduction to programming courses. In this paper we focus on another important gateway course in the computing sequence: Discrete Math. This course involves conceptual problem solving that requires students to think about a problem and conceptually understand it before starting to work on it. These study behaviors might be new to students. During Fall 2021 and Spring 2022 we surveyed over 400 students in two semesters of a Discrete Math course in a large state University. Students answered a questionnaire with 60 questions taken from validated instruments related to the factors mentioned previously. We found that final grades correlated with motivation. Students who were high performing agreed more than low performing students with statements such as "I think that I am going to be pretty good at this class". We also found that final grades correlated with predispositions towards certain learning behaviors. High performing students were more likely than low performing students to be classified as Reflectors. They were more likely to prefer to “try to fully understand the problem first” rather than “start working on the solution immediately”. In future work, we plan to use this to design interventions specifically tailored to such conceptual problem solving classes.

Authors

Prof. Yael Gertner University of Illinois, Urbana-Champaign [biography]

Dr Gertner joined the Computer Science Department at the University of Illinois in 2020 as a Teaching Assistant Professor. She received her B.S. and MEng in Electrical Engineering and Computer Science from MIT, and Ph.D. in Computer and Information Science at the University of Pennsylvania. She was a Beckman Fellow at the University of Illinois Urbana-Champaign. Her current focus is on broadening participation in Computer Science and Computer Science Education She has been developing materials and teaching for iCAN, a new program for broadening participation in CS for students who have a bachelor’s degree in a field other than computer science.
Juan Alvarez University of Illinois, Urbana-Champaign [biography]

Juan Alvarez joined the Department of Electrical and Computer Engineering at University of Illinois faculty in Spring 2011 and is currently a Teaching Assistant Professor. Prior to that, he was a Postdoctoral Fellow in the Department of Mathematics and Statistics at York University, Canada, a Postdoctoral Fellow in the Chemical Physics Theory Group at the University of Toronto, Canada, and a Postdoctoral Fellow in the Department of Mathematics and Statistics at the University of Saskatchewan. He obtained his Ph.D. and M.S. from the Department of Electrical and Computer Engineering at the University of Illinois in 2004 and 2002, respectively. He teaches courses in communications, signal processing and probability. He does research in study skills and response to failure, among other engineering education topics.
Benjamin Cosman University of Illinois at Urbana - Champaign
Dr. Jennifer R. Amos http://orcid.org/0000-0002-9437-8201 University of Illinois, Urbana-Champaign [biography]

Dr Amos joined the Bioengineering Department at the University of Illinois in 2009 and is currently a Teaching Professor in Bioengineering.

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