Authors:
Paul Christ
1
;
Torsten Munkelt
2
and
Jörg M. Haake
1
Affiliations:
1
Department of Cooperative Systems, Distance University Hagen, Universitätsstraße 11, Hagen, Germany
;
2
Faculty of Informatics and Mathematics, HTWD, Dresden, Germany
Keyword(s):
AIG, Automatic Item Generation, Conceptual Modeling, Competency-Based Learning, E-Assessment, E-Learning, Bloom’s Taxonomy, Business Process Modeling, Graph-Rewriting, Generative AI, Large Language Models.
Abstract:
Graphical conceptual modeling is an important competency in various disciplines. Its mastery requires self-practice with tasks that address different cognitive processing dimensions. A large number of such tasks is needed to accommodate a large number of students with varying needs, and cannot be produced manually. Current automatic production methods such as Automatic Item Generation (AIG) either lack scalability or fail to address higher cognitive processing dimensions. To solve these problems, a generalized AIG process is proposed. Step 1 requires the creation of an item specification, which consists of a task instruction, a learner input, an expected learner output and a response format. Step 2 requires the definition of a generator for the controlled generation of items via a configurable generator composition. A case study shows that the approach can be used to generate graphical conceptual modeling tasks addressing the cognitive process dimensions Analyze and Create.