A study of conceptual data modeling in database design: similarities and differences between expert and novice designers

Abstract

This paper explores the similarities and differences between experts and novices engaged in a conceptual data modeling task, a critical part of overall database design, using data gathered in the form of think-aloud protocols. It develops a three-level process model of the subjects' behavior and the differentiated application of this model by experts and novices. The study found that the experts focussed on generating a holistic understanding of the problem before developing the conceptual model. They were able to categorize problem descriptions into standard abstractions. The novices tended to have more errors in their solutions largely due to their inability to map parts of the problem description into appropriate knowledge structures. The study also found that the expert and novice behavior was similar in terms of modeling facets like entities, identifiers, descriptors, and binary and ternary relationships but was different in the modeling of unary relationships and categories. These findings are discussed in relation to the results of previous expert-novice studies in other domains.