This paper presents the results of a project on neural network-based data analysis for knowledge clustering in a second-year course on medical-surgical nursing. Data was collected from 208 nursing students which performed one Multiple Choice Question (MCQ) test at the end of the first term. A total of 23 pattern groups were created using snap-drift. Data obtained can be integrated with an on-line MCQ system for training purposes. Findings about how students are classified suggest that the level of knowledge of the individuals can be addressed by customized feedback to guide them towards a greater understanding of particular concepts.
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