Formative Assessment of Competencies in Remote Laboratories Through Fuzzy Cognitive Maps: A Model for Engineering Education

Abstract

Remote laboratory systems have broadened access to experimentation in engineering education, yet the assessment of the competencies students deploy during practice remains anchored in outcome indicators that overlook the process. The aim of this study was to design and demonstrate a formative assessment model that represents laboratory practice competencies and their causal relationships through fuzzy cognitive maps, integrating the judgement of multiple teaching experts. The research adopted a qualitative approach with a design research strategy, organised into a systematic documentary phase, an expert elicitation phase based on a linguistic scale, and a computational demonstration phase applied to an automatic control practice. Findings show that causal representation identifies nodal competencies, that multi expert aggregation preserves the interpretability of each relationship, and that the priority ordering withstands perturbations of the elicited weights. The model turns the interaction log of the remote laboratory into hierarchical causal feedback and extends competency assessment from a classificatory use towards a formative one.