Graduation Year
2024
Document Type
Dissertation
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Electrical Engineering
Major Professor
Wilfrido A. Moreno, Ph.D.
Committee Member
Ismail Uysal, Ph.D.
Committee Member
Chung Seop Jeong, Ph.D.
Committee Member
Isabela Hidalgo, Ph.D.
Committee Member
Fernando Falquez, Ph.D.
Keywords
Brainwaves, Cognitive Load, Integration, Neurosciences, Personalized Learning
Abstract
This dissertation aims to address the existing gap in the integration of various dimensions within the student learning system, encompassing cognitive, emotional, and physical variables. The primary objective is to construct a Personalized Learning Adaptive Automation model using Electroencephalography (EEG) technology.
To provide deeper insight into the intricate nature of the Human Learning Process, this study introduces a novel analogy with an Industrial Steam Boiler. This analogy serves as a distinctive contribution to research in the field.
The research methodology involved the collection of brainwaves data from engineering students while they undertook educational tasks of varying levels of difficulty, categorized from easy to difficult. The EEG data acquired from the experimental group underwent rigorous analysis to identify statistical patterns associated with beta, alpha, and theta brainwaves at specific sites (F3, F4, P7, and P8). These findings are instrumental in establishing the psychophysiological variables relevant to students’ learning processes in order to be able to analyze the students’ cognitive, emotional, and physical states when selecting the difficulty level of the task that the proposed Hypermedia Adaptive Automation System will deliver accordingly.
The envisioned outcome of this research is the development of a Psychophysiological Hypermedia Adaptive Automation System Model. This model holds significant promise as an optimal, multidimensional, and personalized learning environment. It stands to enhance student development by considering emotional, physical, and cognitive factors, thus offering a holistic approach to education, particularly through the proposed Personalized Learning Adaptive Automation model.
Scholar Commons Citation
Villavicencio López, Liliana María, "Modeling the Human Learning Process Using an Industrial Steam Boiler Analogy to Design a Psychophysiological-Based Hypermedia Adaptive Automation System" (2024). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/10258
