Software engineering as a model of understanding for learning and problem solving

ABSTRACT

This paper proposes a model which explains the process of learning about computation in terms of well-accepted software engineering concepts, and argues that our approach to understanding how problem-solving skills are acquired is an innovation over well-accepted learning theories and models. It examines how it all students make sense of computational processes; by reporting on experimental observations that have been made with school children, and with university undergraduates. We observed little difference between children and adults with regard to how they learn about computation, and suggest that the strong similarities are due to a common set of problem-solving techniques which are fundamental to all problem based learning, in general, and learning about computation, in particular. To conclude, we demonstrate that our model --- based on software engineering concepts --- is useful when reasoning about the relationship between problem solving and learning to program.

REFERENCES

• 1: Harkin, D., On the Mathematical Works of Francois Edouard Anatole Lucas, Enseignement mathematique, 3, 276--288, 1957
• 2: J Paul Gibson, Formal Object Oriented Development of Software Systems Using LOTOS, Stirling University, aug, Thesis CSM-114, 1993.
• 3: J. Paul Gibson, A noughts and crosses Java applet to teach programming to primary school children, Proceedings of the 2nd international conference on Principles and practice of programming in Java, June 16-18, 2003, Kilkenny City, Ireland
• 4: Michael McCracken , Vicki Almstrum , Danny Diaz , Mark Guzdial , Dianne Hagan , Yifat Ben-David Kolikant , Cary Laxer , Lynda Thomas , Ian Utting , Tadeusz Wilusz, A multi-national, multi-institutional study of assessment of programming skills of first-year CS students, Working group reports from ITiCSE on Innovation and technology in computer science education, December 01, 2001, Canterbury, UK [doi>10.1145/572133.572137]
• 5: Borys, S.V. and Spitz, H. H. and Dorans, B. A., Tower of Hanoi performance of retarded young adults and nonretarded children as a function of solution length and goal state., Experimental Child Psychology, 33, 1, 87--110, 1982
• 6: Paul Helman , Robert Veroff, Intermediate problem solving and data structures: walls and mirrors, Benjamin-Cummings Publishing Co., Inc., Redwood City, CA, 1986
• 7: Brainerd, C., Piaget's Theory of Intelligence, Prentice-Hall, Englewood Cliffs, NJ, 1978
• 8: Seymour Papert, Mindstorms: Children, computers, and powerful ideas, Basic Books, Inc., New York, NY, 1980
• 9: Guilford, J. P., The Nature of Human Intelligence, McGraw-Hill, New York, 1967
• 10: Gardner, H., Frames of mind: the theory of multiple intelligences, Basic Books, New York, 1983
• 11: Schoenfeld, A. H., Mathematical Problem Solving, Academic Press, Orlando, Fla", 1985
• 12: Bruner, J. S., Toward a theory of instruction, Belknap Press of Harvard University, Cambridge, Mass, 1966
• 13: Hilgard, E. R. and Bower, G. H., Theories of learning, Appleton-Century-Crofts, New York, 1956
• 14: Bloom, B. S. and Krathowl, D. R., Taxonomy of educational objectives, McKay & Co, New York, 1956
• 15: Stuart K. Card , Allen Newell , Thomas P. Moran, The Psychology of Human-Computer Interaction, Lawrence Erlbaum Associates, Inc., Mahwah, NJ, 1983
• 16: Proceedings of the 7th International Conference on Software Reuse: Methods, Techniques, and Tools, April 2002
• 17: Roy Rada, Software Reuse, Intellect Books, Exeter, UK, 1994
• 18: Gerald C. Gannod , Betty H. C. Cheng, A Framework for Classifying and Comparing Software Reverse Engineering and Design Recovery Techniques, Proceedings of the Sixth Working Conference on Reverse Engineering, p.77, October 06-08, 1999
• 19: O'Kelly, J. and Gibson, J. P., PBL: year One Analysis - Interpretation and Validation, Problem Based Learning 2005, 2005, Lahti, Finland
• 20: J. O'Kelly and A. Mooney and J. Ghent and P. Gaughran and S. Dunne and S. Bergin, An Overview of the Integration of Problem Based Learning into an existing Computer Science Programming Module, Problem-Based Learning International. Conference 2004: Pleasure by Learning, 2004, Jul, Cancun, Mexico
• 21: J. O' Kelly and S. Bergin and S. Dunne and P. Gaughran and J. Ghent and A. Mooney, Initial findings on the impact of an alternative approach to Problem Based Learning in Computer Science, Problem-Based Learning International. Conference 2004: Pleasure by Learning, 2004, Jul, Cancun, Mexico
• 22: William B. Frakes , Sadahiro Isoda, Success Factors of Systematic Reuse, IEEE Software, v.11 n.5, p.14-19, September 1994 [doi>10.1109/52.311045 ]
• 23: William Frakes , Carol Terry, Software reuse: metrics and models, ACM Computing Surveys (CSUR), v.28 n.2, p.415-435, June 1996 [doi>10.1145/234528.234531]
• 24: Joseph M. Morris, A theoretical basis for stepwise refinement and the programming calculus, Science of Computer Programming, v.9 n.3, p.287-306, December 1, 1987 [doi>10.1016/0167-6423(87)90011-6 ]
• 25: Martín Abadi , Leslie Lamport, The existence of refinement mappings, Theoretical Computer Science, v.82 n.2, p.253-284, May 31, 1991 [doi>10.1016/0304-3975(91)90224-P ]
• 26: Martin Abadi , Luca Cardelli, A Theory of Objects, Springer-Verlag New York, Inc., Secaucus, NJ, 1996
• 27: R. J. R. Back , J. von Wright, Contracts, games and refinement, Theoretical Computer Science, v.230 n.1-2, p.259, Jan. 2000
• 28: Judy Kay and Mike Barg and Alan Fekete and Tony Greening and Owen Hollands and Jeffrey H. Kingston and Kathryn Crawford, Problem-Based Learning for Foundation Computer Science Courses, Computer Science Education, 10, 2, 2000, august, 109-128, Routledge, part of the Taylor & Francis Group
• 29: Paul Gibson , Dominique Méry, Fair objects, Object-oriented technology and computing systems re-engineering, Horwood Publishing, Ltd., Chichester, 1999
• 30: Gibson, J. Paul and Méry, Dominque, Teaching Formal Methods: Lessons to be learned, 2nd Irish Workshop on Formal Methods, Cork, Ireland, jul, 1998
• 31: Ekawit Nantajeewarawat , Vilas Wuwongse, Nonmonotonic Inheritance Through Specialisation, Proceedings of the 5th International Conference on Deductive and Object-Oriented Databases, p.423-424, December 08-12, 1997

INDEX TERMS

Primary Classification:
  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.2 Computer and Information Science Education
          Subjects: Computer science education

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms, Experimentation, Human Factors, Theory

Keywords:
computing education research