Who Is Behind the Model? Classifying Modelers Based on Pragmatic Model Features

Jan Mendling, Pnina Soffer, Dirk Fahland, Andrea Burattin, Hajo Reijers, Irene Vanderfeesten, Matthias Weidlich, Barbara Weber

Publication: Scientific journalJournal articlepeer-review


Process modeling tools typically aid end users in generic, non-personalized ways. However, it is well conceivable that different types of end users may profit from different types of modeling support. In this paper, we propose an approach based on machine learning that is able to classify modelers regarding their expertise while they are creating a process model. To do so, it takes into account pragmatic features of the model under development. The proposed approach is fully automatic, unobtrusive, tool independent, and based on objective measures. An evaluation based on two data sets resulted in a prediction performance of around 90%. Our results further show that all features can be efficiently calculated, which makes the approach applicable to online settings like adaptive modeling environments. In this way, this work contributes to improving the performance of process modelers.
Original languageEnglish
Pages (from-to)322 - 338
JournalLecture Notes in Computer Science (LNCS)
Publication statusPublished - 2018

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 502050 Business informatics

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