Dr. Karen Jin is an Assistant Professor of Computer Science at UNH Manchester. Prior to joining UNH in 2012, she held faculty positions over ten years at University of Windsor and Dalhousie University in Canada. She has taught introductory programming as well as more advanced courses in Data Structures, System Programming, Object-Oriented Programming and Design, Software Engineering, Principles of Programming Languages, and Machine Learning. Her hands-on teaching philosophy allows students to put concepts into action, better preparing them with theoretical foundations and practical skills for the workforce.
Dr. Jin's research interests include developing best practices in curriculum and instructional design for teaching computing courses, as well as promoting elementary computational literacy and improving computing education across the entire educational pipeline. She is also interested in novel curriculum design in applied machine learning and data science at both the undergraduate and graduate levels.
Dr. Jin is the faculty advisor for the computing internship program at UNH Manchester. She is also the director and founder of the UNH EPIC (Elementary Program Introducing Computing) program. Since 2013, the EPIC program has partnered with local schools to introduce computational concepts to young students. She has received external funding awards that support the broadening participation of students in computing.
Dr. Jin graduated from Shanghai University with a Bachelor of Engineering in Telecommunications and Electrical Engineering. She earned an M.S. in Computer Science (2001) and a Ph.D. in Computer Science (2010) from the University of Windsor.
Jin, K. H., & Charpentier, M. (2020). Instructional Design and Assessment for Teaching Elementary Students Abstract Computational Concepts. In Society for Information Technology & Teacher Education International Conference. USA: Association for the Advancement of Computing in Education (AACE).
Jin, K. H. (2019). Modular Tasks Design for Teaching Young Students on Physical Computing Platforms. In Unknown Conference (pp. 106-111). doi:10.1145/3349266.3351416
Students’ Misconceptions of Gradient Descent Algorithm in an Machine Learning Course (2019).
Jin, K. H. (2019). Students’ Misconceptions of Gradient Descent Algorithm in an Machine Learning Course. Journal of Computing Sciences in Colleges, 34(6).
Jin, K. H. (2019). Engaging K-12 Learners through Collaborative Physical Computing Project. In Proceedings of the 2019 Research on Equity and Sustained Participation in Engineering, Computing, and Technology - RESPECT'19. Minneapolis, MN,.
Jin, K. H., Eglowstein, H., & Sabin, M. (2018). Using Physical Computing Projects in Teaching Introductory Programming. In Unknown Conference (pp. 155). doi:10.1145/3241815.3241879
Jin, K. H., Haynie, K., & Kearns, G. (2016). Teaching Elementary Students Programming in a Physical Computing Classroom. In Unknown Conference (pp. 85-90). doi:10.1145/2978192.2978238
Jin, K. H., & Kearns, G. (2015). Just Enough Programming for Eight-years Olds (Abstract Only). In Unknown Conference (pp. 675). doi:10.1145/2676723.2691899
Jin, K. H., & Wu, D. (2008). Marginal Calibration in Multi-Agent Probabilistic Systems. In Unknown Conference Vol. 2 (pp. 171-178). doi:10.1109/ictai.2008.70