ABSTRACT
A Concept Inventory (CI) is a set of multiple choice questions used to reveal student's misconceptions related to some topic. Each available choice (besides the correct choice) is a distractor that is carefully developed to address a specific misunderstanding, a student wrong thought. In computer science introductory programming courses, the development of CIs is still beginning, with many topics requiring further study and analysis. We identify, through analysis of open-ended exams and instructor interviews, introductory programming course misconceptions related to function parameter use and scope, variables, recursion, iteration, structures, pointers and boolean expressions. We categorize these misconceptions and define high-quality distractors founded in words used by students in their responses to exam questions. We discuss the difficulty of assessing introductory programming misconceptions independent of the syntax of a language and we present a detailed discussion of two pilot CIs related to parameters: an open-ended question (to help identify new misunderstandings) and a multiple choice question with suggested distractors that we identified.
- V. L. Almstrum, P. B. Henderson, V. Harvey, C. Heeren, W. Marion, C. Riedesel, L.-K. Soh, and A. E. Tew. Concept inventories in computer science for the topic discrete mathematics. SIGCSE Bull., 38(4):132--145, June 2006. Google ScholarDigital Library
- C. H. Crouch and E. Mazur. Peer instruction: Ten years of experience and results. American Journal of Physics, 69:970-977, 2001. Google ScholarCross Ref
- H. Danielsiek, W. Paul, and J. Vahrenhold. Detecting and understanding students' misconceptions related to algorithms and data structures. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education, SIGCSE '12, pages 21--26, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- K. Goldman, P. Gross, C. Heeren, G. L. Herman, L. Kaczmarczyk, M. C. Loui, and C. Zilles. Setting the scope of concept inventories for introductory computing subjects. Trans. Comput. Educ., 10(2):5:1--5:29, June 2010. Google ScholarDigital Library
- R. Hake. Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. Am. J. Phys., 66(1):64--74, January 1998. Google ScholarCross Ref
- G. L. Herman, M. C. Loui, L. Kaczmarczyk, and C. Zilles. Describing the what and why of students' difficulties in Boolean logic. Trans. Comput. Educ., 12(1):3:1--3:28, Mar. 2012. Google ScholarDigital Library
- G. L. Herman, M. C. Loui, and C. Zilles. Creating the digital logic concept inventory. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education, SIGCSE '10, pages 102--106, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- D. Hestenes, M. Wells, and G. Swackhamer. Force Concept Inventory. Phys. Teach., 30(3):141--158, March 1992. Google ScholarCross Ref
- L. C. Kaczmarczyk, E. R. Petrick, J. P. East, and G. L. Herman. Identifying student misconceptions of programming. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education, SIGCSE '10, pages 107--111, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- K. Karpierz and S. A. Wolfman. Misconceptions and concept inventory questions for binary search trees and hash tables. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education, SIGCSE '14, pages 109--114, New York, NY, USA, 2014. ACM. Google ScholarDigital Library
- J. Lazar, J. H. Feng, and H. Hochheiser. Research Methods in Human-Computer Interaction. Wiley Publishing, 2010. Google ScholarDigital Library
- L. Porter, C. Taylor, and K. C. Webb. Leveraging open source principles for exible concept inventory development. In Proceedings of the 2014 Conference on Innovation #38; Technology in Computer Science Education, ITiCSE '14, pages 243--248, New York, NY, USA, 2014. ACM. Google ScholarDigital Library
- J. Sorva. Visual Program Simulation in Introductory Programming Education. PhD thesis, Aalto University, Finland, 2012.Google Scholar
- A. E. Tew and M. Guzdial. The FCS1: A language independent assessment of CS1 knowledge. pages 111--116, 2011. Google ScholarDigital Library
- T. VanDeGrift, D. Bouvier, T.-Y. Chen, G. Lewandowski, R. McCartney, and B. Simon. Commonsense computing (episode 6): Logic is harder than pie. pages 76--85, 2010. Google ScholarDigital Library
- K. C. Webb and C. Taylor. Developing a pre- and post-course concept inventory to gauge operating systems learning. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education, SIGCSE '14, pages 103--108, New York, NY, USA, 2014. ACM. Google ScholarDigital Library
Index Terms
- Developing a Computer Science Concept Inventory for Introductory Programming
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