New Orleans, Louisiana
June 26, 2016
June 26, 2016
June 29, 2016
978-0-692-68565-5
2153-5965
NSF Grantees Poster Session
14
10.18260/p.26911
https://peer.asee.org/26911
599
Firdous Saleheen received the B.Sc. degree in electrical and electronic engineering from Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh, in 2008, and the M.S. degree in electrical engineering from Temple University, Philadelphia, PA, USA in 2013. From 2008 to 2010, he was with Mango Teleservices Ltd., Dhaka, an international IP bandwidth provider of Bangladesh, as a Senior Engineer in the Research and Development Department. He is currently pursuing the Ph.D. degree in electrical engineering in ECE department of Temple University. His research interests include tactile sensation imaging, diffuse optical imaging, biomedical imaging systems development, machine learning, and statistical control theory.
Ph.D student in Temple University, Electrical and Computer engineering department.
William Moser is an M.S.E.E. student at Temple University's Control, Sensor, Network, and Perception (CSNAP) Laboratory.
Vira Oleksyuk received the B.S. degree in electrical and computer engineering with bio-electrical
concentration from Temple University, Philadelphia, PA, USA, in 2013. She is a member of the
Control, Sensor, Network, and Perception Laboratory, Temple University, where she is currently
pursuing the Ph.D. degree in engineering, and a recipient of NSF Graduate Research Fellowship’
13. Her research interests are tactile imaging for breast cancer diagnostic, tissue spectroscopy, digital
image processing, machine learning, and pattern recognition.
Joseph Picone received his Ph.D. in Electrical Engineering in 1983 from
the Illinois Institute of Technology. He is currently a Professor in the Department of Electrical and Computer Engineering at Temple
University. His primary research interests are currently machine
learning approaches to acoustic modeling in speech recognition. His
research group is known for producing many innovative open source
materials for signal processing including a public domain speech
recognition system. He is a Senior Member of the IEEE and has been
active in several professional societies related to human language
technology. He has authored numerous papers on the subject and holds
several patents in this field.
Chang-Hee Won is an associate professor of electrical and computer engineering in the Department of Electrical and Computer Engineering and the director of Control, Sensor, Network, and Perception (CSNAP) Laboratory at Temple University. Previous to coming to academia, he worked at Electronics and Telecommunications Research Institute as a senior research engineer. Currently, he is actively guiding various research projects funded by National Science Foundation, Pennsylvania Department of Health, and Department of Defense. His research interests include stochastic optimal control theory, sensing systems, and virtual laboratory assistant.
The Virtual Open Laboratory Teaching Assistant (VOLTA) has been developed as a personal assistant for students engaged in introductory circuits laboratories. VOLTA consists of pre-laboratory instructions, topic explanation videos, equipment usage videos, circuit simulations, and actual laboratories. This web-based software allows students to perform circuits labs in a self-paced manner. Using VOLTA, students can explore elementary circuit topics in a learning on demand mode. It guides students through the process of building, verifying, and troubleshooting a circuit. VOLTA allows students to do circuits laboratories on demand. Using VOLTA, students can complete a laboratory without a human teaching assistant. Recent enhancements include a circuit comparator and a hardware circuit tracer. The circuit comparator verifies the simulated circuit, while the hardware circuit tracer provides troubleshooting instructions for the hardware circuit. The effectiveness of VOLTA was evaluated by comparing an experimental group of students to a control group of students. The experimental group was taught by VOLTA, while the control group was enrolled in a traditional version of the laboratory. An analysis of variance (ANOVA) test revealed a p-value of <0.001 at a confidence level of 95%, which provides sufficient evidence that the students taught with VOLTA performed better than the control group. VOLTA is an effective teaching tool because it enhances student performance and reduces the workload for human teaching assistants.
Saleheen, F., & Wang, Z., & Moser, W., & Oleksyuk, V., & Picone, J., & Won, C. (2016, June), Effectiveness of Virtual Open Laboratory Teaching Assistant for Circuits Laboratories Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26911
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