Identification and addressing reduction-related misconceptions

References

• Armoni, M. (2009). Reduction in CS: A (Mostly) Quantitative Analysis of Reductive Solutions to Algorithmic Problems. ACM Journal on Educational Resources in Computing, 8, 1–30, article 11.10.1145/1482348
   Google Scholar
• Armoni, M., Gal-Ezer, J., & Hazzan, O. (2006). Reductive thinking in computer science. Computer Science Education, 16, 281–301.10.1080/08993400600937845
   Google Scholar
• Armoni, M., Gal-Ezer, J., & Tirosh, D. (2005). Solving problems reductively. Journal of Educational Computing Research, 32, 113–129.10.2190/6PCM-447V-WF7B-QEUF
   Google Scholar
• Ben-Ari, M. (2001). Constructivism in computer science education. Journal of Computers in Mathematics and Science Teaching, 20, 45–73.
   Google Scholar
• Clancy, M. (2004). Misconceptions and attitudes that interfere with learning to program. In S. Fincher & M. Petre (Eds), Computer science education research (pp. 85–100). London: Taylor & Francis.
   Google Scholar
• Crescenzi, P. (2010). Using AVs to explain NP-completeness. Proceedings of ITiCSE 2010 (p. 299). Ankara.
   Google Scholar
• Crescenzi, P., Enstrom, E., & Kann, V.. (2013). From theory to practice: NP-completeness for every CS student. Proceedings of ITiCSE 2013 (pp. 16–21). Canterbury.
   Google Scholar
• Danielsiek, H., Paul, W., & Vahrenhold, J.. (2012). Detecting and understanding students’ misconceptions related to algorithms and data structures. Proceedings of SIGCSE 2012 (pp. 21–26). Raleigh, NC.
   Google Scholar
• Enstrom, E. (2013). Dynamic programming – Structure, difficulties and teaching. Proceedings of 2013 IEEE Frontiers in Education Conference (pp. 1857–1857). New York, NY.
   Google Scholar
• Enstrom, E., & Kann, V. (2010). Computer lab work on theory. Proceedings of ITiCSE 2010 (pp. 93–97). Ankara.
   Google Scholar
• Eyck, J. T., & Sampath, G. (1998). Incorporating an interactive visualization of NP-completeness proofs into a web-based learning environment. Journal of Computing Sciences in Colleges, 14, 58–69.
   Google Scholar
• Gal-Ezer, J., & Trakhtenbrot, M. (2003). Use of visual tools in distance teaching of computational models. Proceedings of 33rd Frontiers in Education Conference (Session T4F). Boulder, CO.
   Google Scholar
• Gal-Ezer, J., & Trakhtenbrot, M. (2005). Challenges in teaching the pumping lemma in automata theory course. Proceedings of ITiCSE 2005 (p. 369). Monte de Caparica, Portugal.
   Google Scholar
• Gal-Ezer, J., & Trakhtenbrot, M. (2006). Algebraic characterization of regular languages: How to cope with all these equivalences? (p. 325). Proceedings of ITiCSE 2006. Bologna.
   Google Scholar
• Gal-Ezer, J., & Trakhtenbrot, M. (2007). Technology in distance teaching of computational models. Proceedings of 5th Int'l. Conference on Education and Information Systems, Technologies and Applications (pp. 54–59). Orlando, FL.
   Google Scholar
• Gal-Ezer, J., & Zur, E. (2004). The efficiency of algorithms – Misconceptions. Computers and Education, 42, 215–226.10.1016/j.compedu.2003.07.004
   Web of Science ®Google Scholar
• Gotschi, T., Sanders, I., & Galpin, V. (2003). Mental models of recursion. Proceedings of SIGCSE'03 (pp. 346–350). Reno.
   Google Scholar
• Hazzan, O. (2002). Reducing abstraction level when learning computability theory concepts. Proceedings of ITiCSE 2002 (pp. 156–160). Aarhus.
   Google Scholar
• Karavirta, V., Korhonen, A., & Seppälä, O. (2013). Misconceptions in visual algorithm simulation revisited: On UI's effect on student performance, attitudes and misconceptions. Proceedings of Conference Learning and Teaching in Computing and Engineering (pp. 62–69). Macao.
   Google Scholar
• Kim, S., & Park, S. (2013). Teaching NP completeness in secondary schools. Informatics in schools: Local Proceedings of the 6th International Conference ISSEP 2013 (pp. 35–40). Oldenburg.
   Google Scholar
• Lobo, A. F., & Baliga, G. R. (2006). NP-completeness for all computer science undergraduates: A novel project-based curriculum. Journal of Computing Sciences in Colleges, 21, 53–63.
   Google Scholar
• Ozdener, N. (2008). A comparison of the misconceptions about the time-efficiency of algorithms by various profiles of computer-programming students. Computers and Education, 51, 1094–1102.10.1016/j.compedu.2007.10.008
   Web of Science ®Google Scholar
• Pape, C. (1998a). Teaching the reduction technique with interactive visualizations: Report on a simple automatic tutor ( Technical report, pp. 1–12). Karlsruhe: Karlsruhe University.
   Google Scholar
• Pape, C. (1998b). Using interactive visualization for teaching the theory of NP-completeness. Proceedings of ED-MEDIA and ED-TELECOM 98 (pp. 1070–1075). Freiburg, Germany.
   Google Scholar
• Paul, W., & Vahrenhold, J. (2013). Hunting high and low: Instruments to detect misconceptions related to algorithms and data structures. Proceedings of SIGCSE 2013 (pp. 29–34). Denver, CO.
   Google Scholar
• Rodger, S., & Finley, T. (2006). JFLAP – An interactive formal languages and automata package. Sudbury, MA: Jones and Bartlett.
   Google Scholar
• Rosbach, A. H., & Bagge, A. H. (2013). Classifying and measuring student problems and misconceptions. Proceedings of Norsk informatikkonferanse (NIK). Stavanger.
   Google Scholar
• Seppälä, O., Malmi, L., & Korhonen, A. (2005). Observations on student errors in algorithm simulation exercises. Proceedings of 5th Annual Finnish / Baltic Sea Conference on Computer Science Education (pp. 81–86). Helsinki.
   Google Scholar
• Seppälä, O., Malmi, L., & Korhonen, A. (2006). Observations on student misconceptions – A case study of the build – Heap Algorithm. Computer Science Education, 16, 241–255.10.1080/08993400600913523
   Google Scholar
• Sipser, M. (2006). Introduction to the theory of computation. (2nd ed.). Boston: Thomson Course Technology.
   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, 653–667.10.1080/0950069960180602
   Web of Science ®Google Scholar
• Trakhtenbrot, M. (2003). Analysis of typical misconceptions in a theoretical CS course and how to address them in e-learning. Proceedings of ITiCSE 2003 (p. 241). Thessaloniki.
   Google Scholar
• Trakhtenbrot, M. (2013). Students misconceptions in analysis of algorithmic and computational complexity of problems. Proceedings of ITiCSE 2013 (p. 35). Canterbury.
   Google Scholar
• Velazquez-Iturbide, J. A. (2012). Identification and removal of misconceptions about greedy algorithms assisted by an interactive system. Proceedings of International Symposium on Computers in Education (pp. 1–6). Andorra la Vella.
   Google Scholar
• Williams, R. J. (2006). How to understand and create mapping reductions. Retrieved from http://www.ccs.neu.edu/home/rjw/csu390-f06/HowTo/How-to-Create-Mapping-Reductions.pdf
   Google Scholar