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Board 74 : Establish Feedback Loops in an Electrical Engineering Core Course with Adaptive Release

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

Electrical and Computer Division Poster Session

Tagged Division

Electrical and Computer

Page Count

12

Permanent URL

https://peer.asee.org/29635

Download Count

38

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Paper Authors

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Yufang Jin University of Texas, San Antonio

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Dr. Yu-Fang Jin got her Ph.D from University of Central Florida in 2004. After her graduation, she joined the University of Texas at San Antonio (UTSA). Currently, she is a Professor with the Department of Electrical and Computer Engineering at UTSA. Her research interests include nonlinear control and application of nonlinear control and modeling techniques to biological systems. Her teaching interests include course redesigning to improve critical thinking and problem solving capability for engineering education.

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Timothy Yuen University of Texas, San Antonio

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Timothy T. Yuen is an Associate Professor of Instructional Technology in the College of Education and Human Development at the University of Texas at San Antonio. His research investigates how learning technologies and transformative practices can improve learning, engage students, and broaden participation in computer science and engineering.

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Stephanie Ann Garcia University of Texas, San Antonio

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Stephanie Garcia is a Graduate Research Assistant with a MAED from the University of Texas at San Antonio with a concentration in Curriculum and Instruction. Her work with TRESTLE involves training Peer Assisted Learners (PALs) and supporting engineering faculty in implementing culturally relevant pedagogy and other course transformation projects.

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Robin Lynn Nelson University of Texas, San Antonio

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Robin Nelson is a doctoral student in the Department of Interdisciplinary Learning and Teaching and is pursuing a cognate in Instructional Technology at the University of Texas at San Antonio. Her research interests include the development of TPACK in preservice teachers, evidence-based teaching strategies, and the use of gaming in education. She is a Graduate Research Assistant for the TRESTLE project at UTSA.

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Ruitao Jin University of Texas, San Antonio

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Ruitao Jin is a current MS student in the Department of Electrical and Computer Engineering at The University of Texas at San Antonio. His research interest includes system and control and applying deep learning methods to model complex systems.

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Abstract

Analysis and Design of Control Systems is a core course in most Electrical Engineering programs in the United States. This course typically includes topics such as fundamental mathematical background on complex numbers, logarithm calculations, establishing and solving differential equations, Laplace Transform, and new knowledge on stability criteria and controller design. In addition, this course integrates theoretic analysis and real-world applications to prepare students for their senior design projects. However, at our university, a substantial mathematical foundation is required and the lack of this preparation in our student population has been a main source of challenge, resulting in lower student interest, many D and F grades, and substantial withdrawal (DFW) rates in our classes. To address this, we have created an innovative teaching approach to increase students’ performance by differentiating instruction to each student based on his/her understanding of key knowledge points in the lectures measured through an adaptive assessment system. In this approach, we designed a series of online quizzes according to lectures and adaptively assigned exercises/homework to students with respect to their mistakes in their quizzes. For students who did not meet a specific knowledge point, a video lecture was assigned to reinforce the lecture material and a follow-up quiz was designed to examine the improvement in understanding. Instructors would modify the following-up lectures based on the students’ performance in quiz grades in a timely manner and form a fundamental knowledge-point-based feedback loop for instruction. Feedback loops on exam preparation and self-evaluation system were established by pre-exam preparation survey (input), adaptive load for preparation, exam results (output), and post-exam survey (feedback). With this adaptive release mechanism, students were able to review the knowledge points before exams, improve understanding of specific topics with extra work, and efficiently review the course material for exam preparation. This paper presents an evaluation of our course design. Achievement data of 87 students have been collected and analyzed by an evaluation model associating attendance of each student, time spent on exam preparation and homework, number of office hours visits and study groups attended for exam preparation, to outcomes of the course. Our results showed a significant increase in engagement of student and remaining enthusiasms of students partially due to the increase in direct interaction between instructor and individual student by this adaptive release mechanism. In the previous teaching cycles, an average of 18% DFW rate was observed in this course. With the adaptive release, there was no withdraw from the class and DFW rate was reduced to 10%.

(Regular Presentation Preference)

Jin, Y., & Yuen, T., & Garcia, S. A., & Nelson, R. L., & Jin, R. (2018, June), Board 74 : Establish Feedback Loops in an Electrical Engineering Core Course with Adaptive Release Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/29635

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