In digitalized plants, control room operators experience cognitive overload, and literature emphasizes that multimodal physiological integration can better capture operators’ cognitive states. In chemical process operations, current methods often overlook cross-modal interactions. This study used a formaldehyde production simulation with 42 participants exposed to failure scenarios, assessing performance by recovery time and plant status. A novel framework for multimodal physiological integration is proposed, modeling high/low levels of eye-based, skin-related, and cardiovascular metrics using Gaussian distributions. Unique combinations of these metrics are formed, and the overlapping coefficient (OVL) is computed to identify consistent physiological combinations across participants. High-OVL combinations appeared in all optimal, 79% of good, and were negligible in the poor class. Successful participants exhibited distinct cognitive strategies, from low-arousal focus to high-arousal compensation. The Bayesian network estimated participants’ performance-level probabilities, achieving 91% accuracy and robustness to missing data. The framework supports reflective learning, supervisory support, and adaptive training systems.

We use action research to understand the concepts of automating and informating, and study whether agile development practices could be an enabler of informating functionalities. Two questions based on these contrasts and connections motivate this research. Our first research question is: What are the characteristics of an informating functionality and how does an informating functionality differ from an automating functionality? Our second research question is: What role could agile practices play in the development of informating functionalities? In this paper, we discuss the actions, reflections and lessons learned from three action research iterations while developing an admissions application for a business college.