Virtual Conference
July 26, 2021
July 26, 2021
July 19, 2022
NSF Grantees Poster Session
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10.18260/1-2--36836
https://peer.asee.org/36836
434
Dr. Omar Ashour is an Associate Professor of Industrial Engineering at Pennsylvania State University,
The Behrend College. Dr. Ashour received the B.S. degree in Industrial Engineering/Manufacturing Engineering
and the M.S. degree in Industrial Engineering from Jordan University of Science and Technology
(JUST) in 2005 and 2007, respectively. He received his M.Eng. degree in Industrial Engineering/Human
Factors and Ergonomics and the Ph.D. degree in Industrial Engineering and Operations Research from
Pennsylvania State University (PSU) in 2010 and 2012, respectively. Dr. Ashour was the inaugural recipient
of William and Wendy Korb Early Career Professorship in Industrial Engineering in 2016. Dr.
Ashour’s research areas include applied decision making, modeling and simulation, virtual reality, and
process improvement. He contributed to research directed to improve engineering education.
PhD student in Mechanical Engineering at Carnegie Mellon University, with research interests in machine learning and reinforcement learning.
I am an Assistant Professor of Computer Science with an affiliation in Mechanical Engineering at Lafayette College.
I completed my Ph.D. from the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at the Pennsylvania State University, and a Master of Science in Industrial and Systems Engineering from the Rochester Institute of Technology, NY. I worked in the Service and Manufacturing sectors before pursuing m yPh.D.
I am interested in the design and optimization of intelligent decision support systems and persuasive technologies to augment human proficiencies. My research over the last few years has focused on the development of machine learning methods that personalize the human learning process and enhance the efficiency of task completion and decision making.
Conrad Tucker is a professor of mechanical engineering. He focuses on the design and optimization of systems through the acquisition, integration, and mining of large scale, disparate data.
This paper presents the results and findings of the connected learning and integrated course knowledge (CLICK) approach. The CLICK approach aims to provide an integrative learning experience by leveraging virtual reality (VR) and 3D simulation technology. Integrative learning is described as the process of creating connections between concepts (skill and knowledge) from different resources and experiences, linking theory and practice, and using a variation of platforms to help students' understandings. With this approach, the integration is achieved by using a virtual system that mimics a real-life manufacturing or service system. VR and 3D simulation technology are chosen because they enhance visualization, interaction, and collaboration which makes them suitable for educational settings. In addition, immersive technologies provide the sense of being part of the environment. They are effective educational tools because they give students the ability to interact with objects and space in real-time compared to traditional distance, time, or safety constraints. Virtual systems can be designed and created to provide an integrative learning environment via a theme that connects and transfers the knowledge across a curriculum. The paper will focus on the results of the project from two perspectives: technological and educational. The technological perspective will describe the research efforts of automatically generating virtual environments using the reinforcement learning (RL) approach while the educational perspective will summarize the results on the effectiveness of the CLICK approach on students’ motivation, engineering identity, and learning outcomes.
Ashour, O., & Cunningham, J. D., & Lopez, C. E., & Tucker, C. (2021, July), Connected Learning and Integrated Course Knowledge (CLICK) Approach Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--36836
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