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Conclusion This thesis describes research that, although it did not achieve its initial goal of determining the conceptual and practical structure of introductory computer programming, has nevertheless provided a lot of food for thought for anyone interested in the design and evaluation of assessment. Relationships between assessment tasks have been discovered that can point the way to many new research projects. Will teaching students how to test their code improve their programming skills, or is the ability to test a natural extension of their ability to program? Can we manufacture excellent students by explicitly drawing connections between theory-based tasks and practical tasks? The usefulness of competency mapping itself must be considered unproven, rather than disproven, owing to the inappropriate shape of the data that was available to test it. It would not be hard to develop assessment tasks that are more appropriate to the technique, and to do so would give the opportunity of settling the question. If competency mapping is to be further explored, many questions need to be answered: how many students need to be included, and how many assessment tasks should they do? What is an acceptable stress if the results of multidimensional scaling are to make sense? What is the effect of using different algorithms for multidimensional scaling and cluster analysis? As research progresses, more such questions will arise. Given the problems that were experienced with the input data, competency mapping looks like a promising way to analyse student marks data. It produces output that is easy to read, and its implementation is neither expensive nor difficult. In the final analysis, however, the acid test for competency mapping will not be its formal validity, nor its cost, but its usefulness to the university community.

Concept mapping for introductory programming Thesis main page Introduction and background     Background: education     Assessment     Background: concept maps   Aims     Competency mapping     Benefits   Method and results     Data sets     Method     Results     Random data   Analysis and conclusions     Factor analysis     Cluster analysis     Methodological problems     A better test     Conclusion   Appendix I: Datasets Appendix II: Activities Appendix III: MDS coordinates Appendix IV: Data generation scripts   Bibliography

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