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Interactive Editing of Circuits in a Step-based Tutoring System

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

NSF Grantees: Student Learning 2

Tagged Topics

Diversity and NSF Grantees Poster Session

Page Count

16

DOI

10.18260/1-2--34859

Permanent URL

https://peer.asee.org/34859

Download Count

190

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Paper Authors

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Brian J. Skromme Arizona State University

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Dr. Brian J. Skromme is a Professor in the School of Electrical, Computer, and Energy Engineering and was Assistant Dean of the Fulton Schools of Engineering at Arizona State University from 2011-19. He holds a Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign and was a member of technical staff at Bellcore from 1985 to 1989. His research interests are in engineering education, development of educational software, and compound semiconductor materials and devices.

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Caleb Redshaw Arizona State University

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Caleb Redshaw is an undergraduate student of Mechanical Engineering at Arizona State University.

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Abhishek Gupta

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Shatrughn Gupta Arizona State University

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Petru Andrei Florida A&M University/Florida State University

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Dr. Petru Andrei is Professor in the Department of Electrical and Computer Engineering at the Florida A&M University and Florida Stat University (FAMU-FSU) College of Engineering. He is the FSU campus education director for the NSF-ERC Future Renewable Electric Energy Delivery and Management Systems Center (FREEDM) and has much experience in recruiting and advising graduate, undergraduate, REU, and K-12 students, as well as in working with RET teachers. Dr. Andrei has published over 100 articles in computational electronics, electromagnetics, energy storage devices, and large scale optimization methods.

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Hector Erives University of Texas at El Paso

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DeAnna Bailey Morgan State University

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DeAnna Bailey received her B.S (2003) in electrical engineering and D.Eng (2013) from Morgan State University, Baltimore, MD. In 2017, she joined the Electrical and Computer Engineering Department at Morgan State University where she teaches circuit and signal processing classes. Her interest is developing innovative technology that uses artificial intelligence to facilitate and enhance the learning of engineering concepts and principles.

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Willie L. Thompson II Morgan State University

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Dr. Willie L Thompson, II is an Associate Professor of the Electrical and Computer Engineering Department within the Clarence M. Mitchell, Jr. School of Engineering (SOE). Dr. Thompson serves as the Director for the Laboratory for Tactical and Communication Systems, which focuses on research for the design, implementation, and security of advanced wireless embedded systems. Dr. Thompson secured and led the SOE’s first DoD prime contract for the development of a multi-band, multi-mode software-defined radio (SDR) for next-generation DoD telemetry applications. In addition, he led the development of a NASA SDR Testbed for space communication technologies. During his industry tenure, Dr. Thompson served as PI for NASA Goddard Space Flight Center (GSFC) SDR Technology Program from 2005 to 2009 and designed an RF front end for a GPS receiver for Hubble Servicing Mission 2 in 2008. Dr. Thompson has over 15-years of experience in the areas of RF/microwave engineering and communication systems. His technical expertise includes RF/microwave component and circuit design, multi-band transceiver design, software-defined radio, embedded software/firmware, and system-on-chip (SoC) development.

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Srividya Kona Bansal Arizona State University

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Srividya Bansal joined Arizona State University in Fall 2010 as Assistant Professor. Prior to joining ASU she was a Visiting Assistant Professor at Georgetown University in Washington, D.C. She also worked in the industry for 5 years as a Software Engineer at SAP Labs India and Tyler Technologies in Plano, TX. Her primary research focuses on semantics-based approaches for Big Data Integration, Web service description, discovery & composition, and tools for outcome-based instruction design in STEM education. She is also interested in Software Engineering Education research that focuses on experimenting various delivery models in project-centric courses. She designed and developed a Web service description language called USDL (Universal Service-Semantics Description Language). She is the principal investigator of the Instructional Module Development System (IMODS) that is currently under development and funded by National Science Foundation.

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Wendy M. Barnard Arizona State University

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Wendy Barnard is an Assistant Research Professor and Director of the College Research and Evaluation Services Team (CREST) at Arizona State University. Dr. Barnard received her Ph.D. from the University of Wisconsin-Madison, where she focused on the impact of early education experiences and parent involvement on long-term academic achievement. Her research interests include evaluation methodology, longitudinal research design, STEM educational efforts, and the impact of professional development on teacher performance. Currently, she works on evaluation efforts for grants funded by National Science Foundation, US Department of Education, local foundation, and state grants.

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Abstract

A central feature of step-based tutoring systems, which are known to be more effective than conventional answer-based tutoring, is to accept and evaluate each step of a student’s work and provide immediate feedback. In applying this approach to the domain of linear circuit analysis for topics such as superposition, source transformations, and Thévenin/Norton equivalent circuits, it is necessary to allow students to draw or re-draw circuits after killing sources, combining elements in series or parallel or making source transformations, or deriving equivalent circuits, and then provide appropriate feedback (funded by the NSF IUSE program). Here, we describe approaches used for this problem in our tutoring software and systematic assessment of such interfaces. Necessary features involve the ability to “split” or shift a circuit (plotted on a square grid) to make room to place new elements, the ability to drag elements to new positions and reconnect them, and change the type or value of elements or delete them as required in randomly generated circuit topologies. The “sought variables” (unknown currents, voltages, and powers) may also need to be transformed to different quantities to permit series and parallel simplifications. We have also implemented advanced simplification methods that are frequently useful for such problems, including the ability to remove circuit sections that are removably hinged, voltage-splittable, or current-splittable as we discussed in previous work. We will describe approaches and algorithms that implement such functionality and show how it can be used to extend step-based tutoring to situations requiring it. We will further describe automated generation of “transcripts” of student work to overcome a key deficiency of computer-based instruction where students have no record of their work from which to study or review. We have also implemented video tutorials for each exercise to help overcome the learning curve associated with the user interface.

Independent assessment of these features was carried out by professional evaluators in several different participating institutions including those that heavily serve underrepresented minority populations. Evaluations included both quantitative and qualitative methods including focus groups, surveys, and interviews with students and faculty participants. Controlled, randomized experiments were also carried out in a total of three course sections in Spring and Fall 2019 to compare use of the tutoring software to a commercial answer-based system (in Sp’19) and to conventional textbook-based paper homework (in F’19). A total of three advanced tutorials were assessed. In Fall 2019, students rated our software a mean of 4.14/5 for being helpful to learn the material vs. 3.05/5 for the paper homework (HW), p < 0.001 and effect size d = 1.11 σ (N = 43 and 41 in the two groups); rated software difficulty as 2.91 (1=extremely easy, 5 = extremely difficult) compared to 3.83 for the paper HW, p < 0.001 and d = 1.10 σ; and 1.63 for preferring a different HW type for software users (1 = somewhat disagree, 5 = strongly agree) vs. 3.90 for paper HW (p < 0.001 and d = 2.07 σ). Assessment of learning via a post-test quiz and exam is in progress.

Skromme, B. J., & Redshaw, C., & Gupta, A., & Gupta, S., & Andrei, P., & Erives, H., & Bailey, D., & Thompson , W. L., & Bansal, S. K., & Barnard, W. M. (2020, June), Interactive Editing of Circuits in a Step-based Tutoring System Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34859

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