Abstract
Due to the use of thousands of often very complex process models, having them immediately usable towards their purpose is of great economic benefit. In order to maximize usability, process models must be intuitive and easily understandable. In other words, processing the information contained within the process models must enable a successful completion of the task for which the model is being used. Recently, research efforts into the effects of user characteristics on understandability have increased. However, current limitations create promising research possibilities, particularly with regard to the use of realistic process models and direct data collection techniques. This thesis will contribute to the existing body of knowledge by investigating domain knowledge as a fundamental user characteristic and utilizing eye-tracking as a direct data collection method while using realistic, complex process models. The end goal of this research is to propose an automatic process model tailoring technique, with the aim of enhancing a user’s understanding and thus their performance. As of now a pilot study has indicated the existence of distinct reading strategies, which establishes the viability of the proposed future work.
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Acknowledgements
This Ph.D. project will be funded by Ghent University (Belgium) under the supervision of Prof. Dr. Manu De Backer (administrative promoter) and Prof. Dr. Amy Van Looy (daily supervisor).
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Vermeulen, S. (2018). Real-Time Business Process Model Tailoring: The Effect of Domain Knowledge on Reading Strategy. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems. OTM 2017 Workshops. OTM 2017. Lecture Notes in Computer Science, vol 10697. Springer, Cham. https://doi.org/10.1007/978-3-319-73805-5_30
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