Integrating Multidimensional Teaching-Learning Methods: A Trans-disciplinary Approach to Engineering Education Driven by Advances in Neurosciences

This paper underlines the crucial role of transdisciplinary approaches in overcoming complex educational challenges to accelerate learning and meet semiconductor industry workforce needs by combining neurosciences, psychology, education, and engineering. It examines the brain’s learning processes, focusing on sensory and neuroendocrine pathways, and adopts a neuroscience approach from a Control Systems perspective to enhance personalized learning. Using an unconventional analogy between human learning and industrial processes, the study introduces the Psychophysiological-Based Hypermedia Adaptive Automation System (PHAS) model. Employing Model-Based Systems Engineering (MBSE) with Systems Modeling Language (SysML), it develops the Engineering Learning Analytic System (ELAS) framework, featuring a rigorous Verification and Validation (VV) process to align stakeholder needs with system capabilities. ELAS simulations offer predictive insights into soft skill development, facilitating targeted educational interventions. The study aims to develop comprehensive engineers with critical thinking, creativity, problem-solving, communication, and teamwork skills, aligning with initiatives such as the Chips for America by integrating Multidimensional Teaching/Learning Methods to create more effective, inclusive, and holistic educational systems.