Indianapolis, Indiana
June 15, 2014
June 15, 2014
June 18, 2014
2153-5965
Division Experimentation & Lab-Oriented Studies
NSF Grantees Poster Session
14
24.568.1 - 24.568.14
10.18260/1-2--20459
https://peer.asee.org/20459
426
Dr. Brian Skromme is a professor of Electrical, Computer, and Energy Engineering and assistant dean of the Fulton Schools of Engineering at Arizona State University. 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.
Dr. Atkinson is an Associate Professor of Computer Science and Educational Technology with a joint appointment in the School of Computing, Informatics and Decision Systems Engineering in the Ira A. Schools of Engineering and the Division of Educational Leadership and Innovation in the Mary Lou Fulton Teacher’s College at Arizona State University in Tempe, Arizona USA. His current research foci include the design of personalized learning environments, educational applications of social media, mobile learning, learner analytics, and data mining of large multimodal data sets. He has obtained—both independently and collaboratively—over $20 million dollars in grant support from a variety of sources including the NSF, Office of Naval Research and Intel Corporation.
Yih-Fang Huang is Professor of Electrical Engineering and Senior Associate Dean for the College of Engineering at the University of Notre Dame. Dr. Huang received his BSEE degree from National Taiwan University, MSEE degree from University of Notre Dame and Ph.D. from Princeton University. He served as chair of the Electrical Engineering Department at the University of Notre Dame from 1998 to 2006. His research interests focus on theory and applications of statistical signal detection and estimation, and adaptive signal processing.
In Spring 1993, Dr. Huang received the Toshiba Fellowship and was Toshiba Visiting Professor at Waseda University, Tokyo, Japan. From April to July 2007, he was a visiting professor at the Munich University of Technology, Germany. In Fall, 2007, Dr. Huang was awarded the Fulbright-Nokia scholarship for lectures/research at Helsinki University of Technology in Finland.
Dr. Huang received the Golden Jubilee Medal of the IEEE Circuits and Systems Society in 1999, served as Vice President in 1997-98 and was a Distinguished Lecturer for the same society in 2000-2001. At the University of Notre Dame, he received Presidential Award in 2003, the Electrical Engineering department’s Outstanding Teacher Award in 1994 and in 2011, the Rev. Edmund P. Joyce, CSC Award for Excellence in Undergraduate Teaching in 2011, and the College of Engineering’s Teacher of the Year Award in 2013. Dr. Huang is a Fellow of the IEEE.
Dan Robinson is Professor in the School of Education at Colorado State University. He received his Ph.D. in Educational Psychology in 1993 from the University of Nebraska where he majored in both learning/cognition and statistics/research. He has taught at Mississippi State University (1993-1997), the University of South Dakota (1997-1998), the University of Louisville (1998-1999), and the University of Texas (1999-2012). Dr. Robinson has served as the editor of Educational Psychology Review since 2006, and as an editorial board member of nine journals. He has published over 100 articles, books, and book chapters, presented over 100 papers at research conferences, and taught over 100 college courses. His research interests include educational technology innovations that may facilitate learning, team-based approaches to learning, and examining trends in articles published in various educational journals and societies.
Expansion and Evaluation of a Step-Based Tutorial Program for Linear Circuit AnalysisComputer-aided instructional methods offer the possibility to allow students to work individuallyat their own pace, receiving feedback customized to their needs. Step-based tutorial systems, inwhich each step of a student’s work is input and immediately evaluated, are known to besignificantly more effective than traditional answer-based systems that only check thecorrectness of a final answer. Previously, we described the initial development of such a systemfor linear circuit analysis, in which automated algorithms are used to generate an unlimitedsupply of topologically different circuits of selected complexity as well as fully worked, error-free solutions to those exercises. The system accepts and evaluates student input in the form ofequations (entered using a template-based system to guide students), re-drawn circuit diagrams(using a drawing interface), simplified systems of equations, matrix equations, numericalanswers, and waveform sketches (entered using a specially designed drawing interface). Specialpedagogical features such as color coding are included to highlight concepts to students. Atutorial authoring system allows instructors to specify the ways in which sequences of problems,exercises, and expository material are presented to the student. Web-based exercises are used toprovide specific practice on unit conversions and complex number manipulation, two essentialskills in this course. Here, we describe the expansion of this system to cover a broader range oftopics in typical introductory circuit analysis courses. We further discuss the testing of thesystem both in a laboratory-based setting and in live classes, involving over 15 large sections ofa linear circuits class at our institution over several semesters and initial results in classes at threepartner institutions involving a diverse range of student characteristics. All student activity inthe system is logged to a central server, where it is available for later download and analysis.Instructors and administrators have a web-based portal where they can monitor student progress.The laboratory experiments involved a controlled, randomized trial in which we compared theeffectiveness of our system using a pre- and post-test for both a qualitative and quantitative topicto the traditional method of textbook-based problem solving. Students assigned to solveconventional problems increased their test scores by an average of 2.9/100 points, from 58.6 to61.6%, whereas the software users increased by 28.6 points, from 57.8 to 86.4%, a roughly 10Ximprovement. The statistically significant effect size (Cohen d-value) was 1.21 pooled standarddeviations, a very large effect. The Instructional Materials Motivation Survey of Keller wasadministered to both groups, showing a significant 0.53 point higher average for the softwareusers. In classroom use, about 99% of students rated the tutorials as either “very useful” or“somewhat useful” for learning the material, with over 74% considering them “very useful.”Student comments were generally very favorable, with students praising their ability to receiveunlimited practice with no penalty for wrong answers. They appreciated the confidence-buildingaspects of the software as well as the efficiency with which the rapid feedback helped them learnthe material.Fig. 1. Display of successfully entered simplified node equations and corresponding matrixequation. Highlighting of a selected KCL equation and labeling of the currents leaving aselected supernode with arrows that are color-coded to match the color of each term in theequation is also illustrated.Fig. 2. Screen shot of the prototype web-based equation sketching interface, showing apiecewise function that is being entered (and displayed below it).
Skromme, B. J., & Rayes, P., & Cheng, B., & McNamara, B., & Gibson, A. S., & Barrus, A., & Quick, J. M., & Atkinson, R. K., & Huang, Y., & Robinson, D. H. (2014, June), Expansion and Evaluation of a Step-based Tutorial Program for Linear Circuit Analysis Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--20459
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