Using Artificial Intelligence in Academia to Help Students Choose Their Engineering Program
- Conference
- Location
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Baltimore , Maryland
- Publication Date
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June 25, 2023
- Start Date
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June 25, 2023
- End Date
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June 28, 2023
- Conference Session
- Tagged Division
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Computers in Education Division (COED)
- Page Count
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14
- DOI
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10.18260/1-2--44567
- Permanent URL
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https://strategy.asee.org/44567
- Download Count
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220
Paper Authors
Shatha Jawad National University
Dr. Shatha Jawad has more than 22 years of experience in teaching and more than three years as a software engineer. She had UNESCO Fellowship in the field of Information and Communication Technologies, in 2002. Her Ph.D. is in computer engineering. She is a member of the Institute for Learning-enabled Optimization at Scale (TILOS) which has an NSF grant that began on November 1, 2021, for five years. TILOS is a National Science Foundation funded Artificial Intelligence (AI) Research Institute led by the University of California-San Diego and includes faculty from the Massachusetts Institute of Technology, the University of Pennsylvania, the University of Texas at Austin, Yale University, and the National University.
Ronald P. Uhlig National University
From 2010-2014, Dr. Ronald P. Uhlig was Dean, School of Business and Management, National University, La Jolla, CA. He returned to the engineering faculty in 2014 and is currently Chair, Department of Engineering, School of Technology and Engineering. During 2005-2010 he served in multiple positions including Chair of the Department of Computer Science and Information Systems, and Academic Program Director for the Master of Science in Wireless Communications; as well as Principal Investigator for two HP Technology for Teaching grants. From 2000-2005, he was President/CEO, SegWave, Inc., an educational technology systems company he founded.
Previous positions include Vice President for Russia and Eastern Europe, Qualcomm Inc., 1995-99, with offices in San Diego and Moscow, Russia and multiple positions with Northern Telecom and Bell-Northern Research in Ottawa, Canada and Richardson, TX during 1978-1995, including Director, Intelligent Network Solutions and Director, Asia/Pacific Strategic Marketing. He is one of several “Fathers of email”, based on work he did with the US Army and DARPA in the 1970s and several international committees he chaired during 1979-91. Those committees took him to nearly 100 countries globally. He had nationwide responsibility for US Army Materiel Command Scientific and Engineering computing, 1969-78, pioneering many applications in what has become today’s Internet, and he served as a US Army Officer in the Office of the Chief of Staff, in the Pentagon, 1966-1968.
He holds a B.Sc. in Physics from the Massachusetts Institute of Technology, and a Ph.D. in Physics from the University of Maryland. He is the recipient of a Gold Medal from the International Telecommunications Academy for sustained contributions to telecommunications; the Silver Core from the International Federation for Information Processing; and the Founders Award from the International Council for Computer Communications. He has served as a member of the Steering Committee for Project Inkwell.
Pradip Peter Dey
Dr. Pradip Peter Dey has more than 20 years of experience in Computer Science research and education. His university teaching and professional experience emphasizes mathematical modeling, information extraction, syntax and semantics of natural language, w
Mohammad N. Amin National University
Mohammad Amin received his Ph.D. and M.S. degrees in Electrical Engineering & Computer Engineering, and M.S. degree in Solid State Physics from Marquette University, Milwaukee, Wisconsin. He also received M.Sc. and B.Sc. degrees in Physics from Dacca University. Currently, he is working as a Professor of Engineering at the National University, San Diego, California. He received the President Disguised Teaching Award in 2020 and two times President Professoriate Awards. He published and presented 100+ technical papers in the peer reviewed journal and conference proceedings. He edited nine conference proceedings, chaired nine conferences including 2009 ASEE/PSW and 2015 ASEE/PSW and three US Patents.
Bhaskar Sinha National University
Dr. Bhaskar Sinha is a Professor in the School of Engineering and Computing at National University in San Diego, California.
Abstract
It is important to find an area of focus that is related to a career path that aligns with engineering students’ abilities, technical background, and long-term goals. Due to the array of available specializations in industry categories, selecting the best fit for their interests is a big challenge for engineering students. For example, the computer science category includes information technology, programming languages, software engineering, networks, etc. Most departments focus on one industry category and under each category there are concentrations. When students start their journey through college, they focus on a specific concentration that they think they will succeed in. Some students, after starting some of the courses, find that their selected area of focus no longer fits with their abilities or their interests. Some of them try to change their concentration, program, or college, while some of them leave college because they think that their ability is not enough to continue studying. Today, Artificial Intelligence (AI) can be used to improve the education process by helping students learn better and faster when paired with high-quality learning materials and instruction. Also, AI systems can help students get back on track faster by alerting teachers to potential problems. This paper proposes a Deep Learning Neural Networks approach that helps students select their best fit specialization in a specific category. The proposed system will use student data that is related to the general education courses related their programs, such as grades, the number of hours spent on each course's materials, the opinion of the student about the content of each course, and the course(s) that the student enjoyed the most. In addition to data about the general education courses taken by the student, additional data will be taken into consideration, such as the student's preferred specialization and the kinds of materials the student enjoys studying. The proposed Deep Learning Neural Networks system will help students choose a path of study that best fits their abilities and their goals, and that prepares them for successful careers.
Jawad, S., & Uhlig, R. P., & Dey, P. P., & Amin, M. N., & Sinha, B. (2023, June), Using Artificial Intelligence in Academia to Help Students Choose Their Engineering Program Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--44567
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