Prof. Dr. Jana-Rebecca Rehse, Universität Mannheim: „User Behavior Mining: Applying Process Mining to Analyze Human Behavior Change“
User behavior mining (UBM) refers to the application of process mining and machine learning techniques to user interaction logs, i.e., high-resolution event logs that record low-level, manual activities performed by a user during the execution of a task in a software system. By analyzing these event logs, UBM can generate detailed insights into how users of a software interact with its user interface. Those insights can be used for technical purposes, such as the mitigation of software usability issues or the automation of process execution. In addition, UBM is a valuable tool for researchers who want to understand or predict a specific aspects of user behavior. It provides a data-driven, non-intrusive method to obtain a holistic view on the behavior of software users over a longer period of time. Researchers can hence use UBM to gather and analyze empirical data, which can be used to test, support, refute, or develop behavioral theories.
In this talk, I present UBM as an analytical method for examining user behavior in software systems. In the first part, I conceptualize it by means of the four-part UBM framework, which elaborates (1) how UBM data can be captured, (2) which technologies can be applied to analyze it, (3) which objectives UBM can accomplish, and (4) how theories can guide the analytical process. In the second part, I show how we applied UBM to analyze behavior changes in a mobile health (mHealth) scenario. Based on social cognitive theory (SCT), we apply process discovery and sequential rule mining to investigate how people interact with mHealth apps and how these interactions affect their physical activity. We find that by applying UBM, we can empirically substantiate implicit assumptions of SCT and derive recommendations for the optimal design of mHealth apps.
Dr. Jana-Rebecca Rehse is Junior Professor for Management Analytics at the University of Mannheim. Her research focusses on data-driven business process management by means of process mining and machine learning, particularly on methods for process analysis, process assistance and process automation for (business) value. Her research results, funded by the DFG and the BMBF, have been published in more than 40 conference and journal papers so far. From 2015 to 2020, Dr. Rehse was a researcher and project lead at the Institute for Information Systems (IWI) at the German Research Center for Artificial Intelligence (DFKI) in Saarbrücken. In 2019, she obtained her PhD from Saarland university with a thesis titled “Leveraging Artificial Intelligence for Business Process Management”. Dr. Rehse holds a bachelor’s and master’s degree in business informatics from Saarland University. In 2014, she spent six months as a visiting scholar at Stevens Institute of Technology in Hoboken, NJ. USA.