ABSTRACT
The knowledge about misconceptions of programming beginners can help the instructors to improve their lessons and exercises and to eliminate barriers to learning. However, there is not much research about learning barriers, like misconceptions, in computer science education. This paper explains the goals and first results of our survey in this area. We interviewed 60 students in a pretest and 110 students in a test [8] to observe whether misconceptions about iterations and runtime are following underlying intuitive rules. Our results are verifying an underlying rule and unveiling two new misconceptions, which -- to the best of the authors' knowledge - have not been mentioned in literature yet. The results could help teachers to prevent learners' misconceptions.
- Danielsiek, H., Paul, W., Vahrenhold, J. 2012. Detecting and understanding students' misconceptions related to algorithms and data structures. In Proc. 43rd SIGSCE Comp. Sci. Ed., pp. 21--26. Google ScholarDigital Library
- Fleury, A. E. 2000. Programming in Java: Student-constructed rules. SIGCSE Bulletin, 32(1), 197--201. Google ScholarDigital Library
- Gal-Ezer, J., Zur, E. 2003. The efficiency of algorithms--misconceptions. Computers & Education, 42(3), 215--226. Google ScholarDigital Library
- Holland, S., Griffiths, R., Woodman, M. 1997. Avoiding Object Misconceptions. SIGCSE Bulletin, 29(1), 131--134. Google ScholarDigital Library
- Mayring, P. 2000. Qualitative Content Analysis, Forum Qualitative Sozialforschung, 1 (2), Art. 20.Google Scholar
- Mayring, P. Qualitative Inhaltsanalyse: Grundlagen und Techniken 2008, Beltz, 2008.Google Scholar
- Ragonis, N., Ben-Ari, M., 2005. A long-term investigation of the comprehension of OOP concepts by novices. Computer Science Education, 15(3), 203--221. DOI= http://dx.doi.org/10.1080/08993400500224310Google ScholarCross Ref
- Schiek, D. 2014. The Written Interview in Qualitative Social Research. Zeitschrift für Soziologie, 43(5), 379--395.Google Scholar
- Stavy, R., Tirosh, D. 1996. Intuitive rules in science and mathematics: the case of `more of A-more of B'. International Journal of Science Education, 18(6), 653--6.Google ScholarCross Ref
Index Terms
- Searching for Barriers to Learning Iteration and Runtime in Computer Science
Recommendations
Educational software for improving learning aspects of Newton's Third Law for student teachers
In this paper, we present the design, development, implementation and evaluation of educational software "Newton-3", aiming at the learning of Newton's Third Law by student-teachers who are not Physics majors. We describe the theoretical issues of our ...
Motivational active learning for computer science education (abstract only)
SIGCSE '14: Proceedings of the 45th ACM technical symposium on Computer science educationMotivational Active Learning (MAL) is an innovative pedagogical approach based on MIT's teaching format TEAL (Technology-Enabled Active Learning) combined with advanced motivational strategies based on gamification design aspects. The main idea of MAL ...
An Experimental Method for the Active Learning of Greedy Algorithms
Greedy algorithms constitute an apparently simple algorithm design technique, but its learning goals are not simple to achieve. We present a didactic method aimed at promoting active learning of greedy algorithms. The method is focused on the concept of ...
Comments