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Multi-facet Problem Comprehension: Utilizing an Algorithmic Idea in Different Contexts

  • Bruria Haberman
  • Orna Muller
  • Haim Averbuch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5090)

Abstract

Instructional design has a significant influence on the construction of knowledge, especially for novices. Specifically, the way the instruction of algorithmic problem solving is designed has a significant effect on the development of the student’s capabilities to analyze and solve problems. We present a pedagogical approach regarding teaching algorithmic problem solving, which is based on the assimilation of a new concept by demonstrating its different facets through a variety of relevant examples. The approach aims to support multi-facet problem comprehension, as well as to enhance the student’s ability to utilize algorithmic ideas in different contexts. The approach was introduced to computer science teachers through a workshop activity aimed at discussing the topic of evaluating the complexity level of problems and their challenging characteristics. We think that an activity of this kind is beneficial for raising teachers’ awareness of the way they select problems in order to develop students’ problem-solving skills.

Keywords

Algorithmic problem solving Instructional design 

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References

  1. 1.
    Fleury, A.E.: Encapsulation and reuse as viewed by Java students. In: Proceedings of the 31th SIGCSE Technical Symposium on CS Education, pp. 189–193 (2001)Google Scholar
  2. 2.
    Ginat, D., Haberman, B., Cohen, D., Catz, D., Muller, O., Menashe, E.: Patterns in computer science. Tel- Aviv University (in Hebrew) (2001)Google Scholar
  3. 3.
    Hoare, C.A.R., Jones, C.B.: Essays in Computing Science. Prentice-Hall International, Englewood Cliffs (1989)zbMATHGoogle Scholar
  4. 4.
    Linn, M.C., Clancy, M.J.: The case for case studies of programming problems. Communications of the ACM 35(3), 121–132 (1992)CrossRefGoogle Scholar
  5. 5.
    Marshall, S.P.: Schemas in problem solving. Cambridge University Press, New York (1995)Google Scholar
  6. 6.
    Muller, O., Haberman, B., Averbuch, H.: (An almost) pedagogical pattern for pattern-based problem-solving instruction. In: Proceedings of ITiCSE 2004, Leeds, UK, pp. 102–106 (2004)Google Scholar
  7. 7.
    Muller, O.: Pattern oriented instruction and the enhancement of analogical reasoning. In: Proceedings of the 1st International Computing Education Research (ICER) Workshop, pp. 57–67 (2005)Google Scholar
  8. 8.
    Muller, O.: The effect of pattern-oriented instruction in computer-science on algorithmic problem-solving skills. Doctoral dissertation, Tel-Aviv University, Israel (2007)Google Scholar
  9. 9.
    Perkins, D.N., Martin, F.: Fragile knowledge and neglected strategies in novice programmers. In: Soloway, E., Iyengar, S. (eds.) Empirical Studies of Programmers, pp. 213–229. Albex Publishing Corporation, Norwood, New Jersey (1986)Google Scholar
  10. 10.
    Rist, R.S.: Schema creation in programming. Cognitive Science 13, 389–414 (1989)CrossRefGoogle Scholar
  11. 11.
    Robins, A.: Transfer in cognition. Connection Science 8(2), 185–203 (1996)CrossRefGoogle Scholar
  12. 12.
    Robins, S., Mayer, R.E.: Schema training in analogical reasoning. Journal of educational Psychology 85(3), 529–538 (1993)CrossRefGoogle Scholar
  13. 13.
    Samurcay, R.: The concept of variable in programming: its meaning and use in problem-solving by novice programmers. In: Soloway, E., Spohrer, J.C. (eds.) Studying the Novice Programmer. Lawrence Erlbaum Associates, Hillsdale (1989)Google Scholar
  14. 14.
    Soloway, E.: From problems to programs via plans: the content and structure of knowledge for introductory lisp programming. J. Educational Computing Research 1(2), 157–172 (1985)Google Scholar
  15. 15.
    Wing, J.M.: Computational thinking. Communication of the ACM 49(3), 33–35 (2006)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Bruria Haberman
    • 1
  • Orna Muller
    • 2
  • Haim Averbuch
    • 3
  1. 1.Computer Science DepartmentHolon Institute of Technology, and, Davidson Institute of Science Education, The Weizmann Institute of ScienceRehovotIsrael
  2. 2.Science Education Department, School of EducationTel-Aviv University, and Software Engineering Department, Ort Braude College of EngineeringKarmielIsrael
  3. 3.Computer Science Group, Science Education Department, School of EducationTel-Aviv UniversityIsrael

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