Department of Computer Engineering
Enhancing Student Practice in CS Education
Ph.D. Student at the School of Computing and Information University of Pittsburgh
In recent years, the enrollment and diversity in Computer Science (CS) introductory classes have increased considerably. In an introductory CS course, students come from several majors and usually differ substantially in their prior knowledge, motivation, expectations, and goals. To eliminate such differences, instructors typically provide extra practice opportunities for less-prepared students to enhance their CS skills; and to support such practice needs, CS education (CSE) researchers and practitioners have developed a range of learning tools. The main problem here is that these tools are expected to be used by the students in a non-mandatory setting (not graded). However, despite the educational value of these tools, as shown in several studies, only a fraction of students use these tools in a non-mandatory practice mode and an even smaller fraction use them regularly. If these tools fail to keep students engaged and be persistent on relatively simple learning tasks, achieving more complex learning objectives becomes impossible. In CSE, researchers recognized the engagement problem and explored several approaches to improve student engagement in a non-mandatory practice context. In this talk, I will present our recent works in this direction and discuss ways to understand student engagement better and enhance voluntary practice in CS education.
Bio: Kamil Akhuseyinoglu is a Ph.D. student in the School of Computing and Information at the University of Pittsburgh, USA. He is a member of the Personalized Adaptive Web System (PAWS) lab and working at NSF-supported computer science education projects. In his research, he designs and develops personalized learning tools for CS education -specifically for programming- to explore how students interact with them, focusing on motivation and engagement to support and improve the learning process. His research interests include adaptive educational systems, learner control, self-regulated learning, user-modeling, educational data mining, and explainable recommendations. He received his M.S. degree in Information Systems from Middle East Technical University and his B.S. degree in Computer Engineering from Bilkent University.
DATE: 08 November 2021, Monday @ 13:30