Function-centered modeling of complex non-physical systems
We are happy to announce the publication of our latest paper!
In Function-Centered Modeling of Complex Non-Physical Systems: An Exploratory GTST-MLD Application to an Unstructured System for Transformative Change, we address the challenge of representing and analyzing complex non-physical systems, particularly those that are human-centered and adaptive. Such systems are difficult to formalize because their behavior emerges from interdependent conceptual, methodological, and organizational elements rather than from physical components. To respond to this challenge, the work extends the Goal Tree–Success Tree and Master Logic Diagram (GTST-MLD) framework beyond its traditional engineering applications, demonstrating its applicability to domains where structure must coexist with human variability.
Using the teaching–learning process in Higher Education as an illustrative case, the study shows how a function-centered modeling approach can be used to decompose system complexity into explicit and traceable dependencies. The modeling process was informed by a mixed-methods design that combined a systematic literature review, expert interviews, and survey-based validation, ensuring both conceptual grounding and practical relevance. Rather than treating education as a linear process or a set of isolated outcomes, the model captures the relationships among pedagogical strategies, conceptual progression, and learning objectives within a coherent dependency structure.
A key contribution of the work lies in its ability to make pedagogical bottlenecks visible by explicitly defining functional nodes and their interconnections. The model does not aim to directly quantify learning gains; instead, it provides a structured representation of the pathways that influence learner engagement, conceptual integration, and adaptability. Within this bounded context, the framework enables a clear alignment between Threshold Concepts, Learning Outcomes, and methodological strategies, supporting transparency and analytical consistency.
By establishing a reproducible and auditable modeling procedure for non-physical systems, the study offers a foundation for diagnosing system behavior and supporting informed design decisions. While the focus is on representational validity rather than outcome measurement, the resulting framework creates a basis for future empirical evaluation and iterative optimization of complex human-centered systems.