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Method and results To generate a concept map, cluster analysis and multidimensional scaling are applied to proximity data generated from the number of times concepts were clustered together. Competency maps are generated in a similar way: after student marks data is collected, cluster analysis and multidimensional scaling are applied to proximity data generated from the matrix of correlations between the marks. We were able to use SPSS for the generation of the correlation matrix, the cluster analysis and the multidimensional scaling. The first stage in the construction of the competency map was the acquisition of student marks data. Ethics committee approval was sought and gained to use the results from CSE1301 Computer Programming, semester 1, 2001. This is the first computer science subject that students undertake in a Computer Science degree at Monash University. Between three and four hundred students each year undertake this subject.

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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