The MDS-ANAVA technique for assessing knowledge representation differences between skill groups

Knowledge representation is one of important factors that determine human performance on cognitive tasks. Due to different levels of experience, different groups of people may develop different knowledge representations which lead to different levels of performance on cognitive tasks. If knowledge representation differences exist between skill groups such as experts and novices, those differences can be used to guide the training of novices for skill acquisition, and to assist the design of jobs and tools for performance enhancement. A technique is presented in this paper for assessing knowledge representation differences between skill groups, based on multidimensional scaling (MDS) of dissimilarity data and analysis of angular variance (ANAVA). The MDS-ANAVA technique was applied to two sets of dissimilarity data that were obtained from ten experts and ten novices in the computer domain, one set concerning 23 concepts in C computer programming, and another set concerning 21 concepts in the UNIX operating system. Knowledge representation differences from the MDS-ANAVA technique are compared with those from the hierarchical clustering technique. The MDS-ANAVA technique shows several advantages to the hierarchical clustering technique in testing the statistical significance of knowledge representation differences between skill groups and revealing features underlying knowledge representations of skill groups