Karen Jin

Karen Jin

Associate Professor
Phone: (603) 641-4398
Office: Applied Engineering & Sciences, 88 Commercial Street, RM 139, Manchester, NH 03101

Dr. Karen Jin is an Associate Professor of Computer Science at UNH Manchester. She has taught introductory programming as well as 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. She is especially interested in novel curriculum design in applied machine learning and data science courses 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 New Hampshire faculty coordinator for the NCWIT Aspiration in Computing Award, which aims to promote underrepresented high school students in pursuing tech careers. She has received external funding awards that support the broadening participation of students in computing.

Courses Taught

  • COMP 405: Intro to Web Design & Devel
  • COMP 574: Applied Computing 2
  • COMP 690: Internship Experience
  • COMP 690/890: Internship Experience
  • COMP 690/891/892: Internship Experience
  • COMP 740/840: ML Applications/Tools
  • COMP 840: ML Applications/Tools
  • COMP 890: Internship
  • COMP 891: Internship Practice
  • COMP 892: Applied Research Internship
  • COMP 898/899: Master's Project
  • COMP/DATA 690: Internship Experience
  • COMP/DATA 690/891/690: Internship Experience
  • COMP/DATA 690/891/892/690: Internship Experience
  • COMP/GRAD 898/899/900: Master's Project
  • COMP/GRAD 898/900: Master's Project
  • DATA 690: Internship Experience
  • GRAD 900: Master's Continuing Research
  • UMST 500: Internship

Education

  • Ph.D., University of Windsor, Ontario
  • M.S., University of Windsor, Ontario
  • B.S., Shanghai University

Research Interests

  • Artificial Intelligence
  • Computing Education

Selected Publications

  • Jin, K. H., & Charpentier, M. (2020). Automatic Programming Assignment Assessment Beyond Black-box Testing. J. Comput. Sci. Coll.. Retrieved from http://dl.acm.org/citation.cfm?id=3205191.3205223

  • 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). Integrated CS for Teachers in Pre-Secondary Schools. 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. (2018). Students’ Understanding of Basic Computational Concepts in an Introduction to Mobile Development Course. J. Comput. Sci. Coll., 33, 183-185. Retrieved from http://dl.acm.org/citation.cfm?id=3205191.3205223

  • Jin, K., & Pimentel, D. S. (2018). High-Challenge and Low-Stakes: On Improving Elementary Students’ Self-Efficacy in Computing. In E. Langran, & J. Borup (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 2018 (pp. 25-30). Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/p/182497

  • Jin, K. H. (2018). A "Loopy" Encounter. In Unknown Conference (pp. 1099). doi:10.1145/3159450.3162306

  • Building uSafeNH mobile app: the evolution of an undergraduate project over multiple semesters (2017).

  • 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

  • Most Cited Publications