Understanding Knowledge Models: Modeling Assessment of Concept Importance in Concept Maps

Concept mapping is widely used in educational and other settings to aid knowledge construction, sharing, and comparison; concept maps are also used as a vehicle for assessing understanding. To aid the concept mapping process, projects at Indiana University and the Institute for Human & Machine Cognition (IHMC) are developing “intelligent suggesters” to support users as they build concept maps, by presenting them with relevant information from existing knowledge models and the Internet. This depends on identifying important concepts in the concept map under construction. This paper presents and evaluates models of the influence of concept map layout and structure on the selection of concepts expected to be relevant to the topic of concept maps. It presents and assesses a set of potentially-relevant structural factors and evaluates how these factors combine to affect human judgments of concept importance. Twenty subjects were asked to judge the relative importance of concepts in concept maps selected to highlight particular characteristics, and three models were compared to their judgments. Analysis of the results shows that subjects were significantly influenced by concept map topology, but little influenced by other aspects of concept map layout. The results suggest that layout-independent models of concept maps can provide a suitable representation for guiding retrieval of topicrelevant information to support concept map construction, provided that the representation reflects topologically-based influences. The results are applied in the design of the suggesters’ similarity assessment procedures for retrieving relevant concept maps.