Much research has been done on the comprehension and development of visual business process models. In related areas such as linguistics and software engineering, researchers have used techniques from neuroscience to study the physiological and neurological processes when working with text in tasks such as program code debugging and natural language understanding. Such techniques have only to a limited degree been used to improve our understanding of visual conceptual models. In this paper, we will present ongoing research on using techniques for collecting biometric data to investigate how we work with visual conceptual models. We will provide results from an experiment collecting biometric data from 57 people as they respond to comprehension questions in connection with process models. The approach, based on techniques used in multi-modal learning analytics (MMLA), investigates how performance on modelling tasks is correlated with biometric data, collecting data in parallel from EEG, eye-tracking, wristbands, and facial expression (through cameras). We find that improved understanding of the performance of modelling tasks can be achieved by using biometric data in a close-to-natural usage situation. Results from the experiment can also be the basis for providing a neuro-adaptive modelling tool. We have just started initial work on this topic, and we present the start of a larger research program in this area in the concluding remarks.

Processes often exhibit significant variability, posing challenges for process discovery and insight extraction. While most studies focus on detecting variability over time (e.g., concept drift), control-flow variability can also manifest across other dimensions, such as case durations or performance metrics. Identifying and understanding these changes is vital for uncovering inefficiencies and undesired behaviors. This paper introduces a novel framework that combines control-flow change detection across performance dimensions with explainability, providing insights into where and how control flow evolves. The framework uses a sliding window approach with the earth mover’s distance to detect behavioral shifts. To enhance interpretability, event logs are encoded into a feature space defined by declarative constraints, capturing intuitive control-flow properties. Clustering these features reveals distinct behavioral patterns and their evolution along performance dimensions, linking detected changes to specific process dynamics. We validate the framework using three real-life event logs, including one from the UWV employee insurance agency in the Netherlands, demonstrating its ability to uncover meaningful changes, explain process variability, and support data-driven decision-making. The framework is implemented as an open-source tool for broader applicability.

Jjodel is a cloud-based reflective platform designed to address the challenges of Model-Driven Engineering (MDE), particularly the cognitive complexity and usability barriers often encountered in existing model-driven tools. This article presents the motivation and requirements behind the design of Jjodel and demonstrates how it satisfies these through its key features. By offering a low-code environment with modular viewpoints for syntax, validation, and semantics, Jjodel empowers language designers to define and refine domain-specific languages (DSLs) with ease. Its innovative capabilities, such as live co-evolution support, and syntax customization, ensure adaptability for academic and industrial contexts. A practical case study of an algebraic expression language highlights the ability of Jjodel to manage positional semantics and event-driven workflows, illustrating its effectiveness in simplifying complex modeling scenarios. Built on modern front-end technologies, Jjodel aims to operationalize concepts from MDE research into a usable platform that supports a range of modeling tasks.

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