(Business Process) Models from an Educational Perspective

A vast amount of scientific research and literature is devoted to business process models. The spectrum includes languages, style, quality, analysis, simulation methods, tool support, and even further aspects. However, most of this work only considers business process models from an industrial perspective: the ultimate goal is to support organizations and companies through the development, use and implementation of process models. But as business process modeling, or even more generally modeling itself, has become an integral part of many programs in higher education institutions and universities, we have identified yet other challenges and usage opportunities for research with regards to models in the context of modeling education. Year by year during our university courses concerned with modeling, a growing number of students creates models in exercises and exams. Each semester we struggle with handling and grading these big yet analogous datasets with hundreds of modeling solutions for, e.g., business processes and ER-diagrams. However, a manual correction and grading procedure is usually a time-consuming and error-prone task. Also, the consistent application of a predefined grading scheme is hard to enforce. Currently we often distribute the correction of exam questions so that only one person is responsible for one question to increase consistency while trying to be as efficient as possible. However, we still detect inconsistent or even erroneous corrections. In the age of growing digitization and Massive Open Online Courses (MOOCs) many universities start integrating IT support into the academic processes, e.g., by offering e-assessments [6]. As opposed to written exams or tests, in an eassessment students create digital solutions using a software tool so that the results can be analyzed (semi-)automatically. Thus, having models in digital form rather than handwritten on a sheet of paper opens up new opportunities for the educational context. First, an automation of the exam correction process enables an efficient and consistent grading. Secondly, we can conduct what is widely denoted with the buzzword Learning Analytics [3]. The results of automated analyses can be easily aggregated over large sets of models to, e.g., detect the most common mistakes. This in turn allows us to draw conclusions about the underlying teaching concept and can be used to improve university courses. With this article, we aim to illustrate those opportunities.