Stefan Strecker

Conceptual modeling is a learning task essential to students of computer science, software engineering and related programs. Construed as a complex task, surprisingly little is known about the actual act of conceptual modeling, and about modeling difficulties learners experience. Combining complementary modes of observation of learners’ modeling processes, we study modeling difficulties encountered while performing a data modeling task. Using the concept of cognitive breakdown, we analyze audiovisual protocols of the individual modelers’ modeling processes, recordings of their interactions with the employed modeling software tool and survey data of modelers about their perception of encountered modeling difficulties. In an exploratory study and a follow-up study, we identify eight types of modeling difficulties related to modeling entity types, generalization hierarchies, relationship types, attributes and cardinalities. The identified types of modeling difficulties contribute to a better and more complete understanding of data modeling processes intended to inform design science research on modeling assistance for data modelers at different stages of their learning and mastering of conceptual data modeling.

What do (non-)experienced modelers reason while conceptual modeling and how do they arrive at modeling decisions, which modeling and learning difficulties do they face and why, and how do they overcome these difficulties by tailored modeling tool support are questions of relevance and importance to practicing modelers and, likewise, to conceptual modeling research. For the past 7 years, we have been designing, developing, and evaluating a modeling tool integrating a research observatory aimed at studying individual modeling processes online, in the field, and under laboratory conditions—to contribute to a richer understanding of modeler reasoning and decision-making, to identify common modeling and learning difficulties, and, ultimately, to design tool support to mitigate difficulties and to improve assistance for (non-)experienced modelers. We present an overview of the modeling observatory and of a corresponding multimodal observation setup.