A Bloom's Taxonomy-Based Model for Assessing Learning Outcomes in Virtual Reality Engineering Laboratories

Abstract

Virtual reality (VR) laboratories are increasingly used in engineering education to compensate for restricted access to real industrial equipment, yet reliable methods for assessing the learning outcomes they produce remain underdeveloped. This study proposes and empirically validates a two-tier assessment methodology for VR-based laboratory training in the course Fundamentals of Electric Power Supply. Passive (observational) laboratories are scored with a linear model weighting response accuracy and response speed (0.75/0.25), while active (hands-on) laboratories are scored with a five-criterion model—time, safety, task completion, operational speed, and quality—whose weights were derived objectively through the Shannon entropy method and mapped onto the six cognitive levels of Bloom’s revised taxonomy. The model was validated through a quasi-experimental study with 105 undergraduates at three Uzbek universities (53 experimental, 52 control). The experimental group’s mean achievement (87.7%) significantly exceeded the control group’s (74.3%), yielding an effectiveness coefficient of 1.18 (Student’s t-test, α = 0.05). Findings support the model as a psychometrically grounded, transferable tool for competency-based assessment in immersive engineering education.