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.

• Publication year

  2024

• Venue

  2024 IEEE Latin American Electron Devices Conference (LAEDC)

• Publication date

  2024-05-08

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/LAEDC61552.2024.10555849
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Leveraging Multicriteria Integer Programming Optimization for Effective Team Formation

  2025cited by this paper
• “Accelerating Engineering Education and Workforce Development in Automation & Control for the Semiconductor Industry Based on Cognitive Neuroscience”

  2023cited by this paper
• Synthetic Data Generation for Engineering Education: A Bayesian Approach

  2023cited by this paper
• The Validity of Physiological Measures to Identify Differences in Intrinsic Cognitive Load

  2021cited by this paper
• On Neuroeducation: Why and How to Improve Neuroscientific Literacy in Educational Professionals

  2021cited by this paper
• Individual differences in the learning potential of human beings

  2017cited by this paper
• Analysis of Cognitive Load -- Importance of EEG Channel Selection for Low Resolution Commercial EEG Devices

  2014cited by this paper
• Model-based systems

  2008cited by this paper
• National Institute of Standards and Technologyにおける超伝導研究及び生活

  2001cited by this paper

• No citing papers are available for this paper.