In this paper, we report a case study on employing and adapting a teaching method based on topical guide objectives (TGOs) in a senior-level undergraduate computing engineering course. This method was first introduced by Dr. Mattew Morrison at the University of South Florida. According to this method, course materials are divided into a list of TGOs. Homework assignments are given to students at the end of every lecture. The assignments are designed explicitly around the TGOs that have been covered by each lecture. Each TGO consists of a learning objective, a set of key-points and basic concepts, relationship between them, and one or more exercise problems.
Typically, engineering/science homework is in the form of a set of problems for students to solve. The drawback of this approach is that students often get buried in the technical details and forget about the key points and concepts taught in the lectures. This new form of assignment encourages students to focus on key points and concepts they learned in the lectures, and learn how to apply them to solve complicated problems. Furthermore, this teaching method informs students which concepts are fundamentally important. It helps students understand the wording used on quizzes and exams. It also helps build up a positive relationship between students and the instructor such that students could focus on learning instead of testing.
When employing this TGO-based teaching method, we made some improvements over the original method. In the homework assignments, for each TGO, only the concept names are given and students are asked to elaborate them in their own words as part of the homework. This would force them to learn the concepts and have the ability to recite/paraphrase them. In our case study, we find the TGO-based teaching method is particularly effective in communicating with students that are less prepared and less motivated. Both grade-based and survey-based evaluations show that student performance increased significantly by focusing on learning instead of testing, by using clear written communication via homework assignments, and by applying constant pressure.
Dr. Zhao is a Full Professor at the Department of Electrical Engineering and Computer Science, Cleveland State University (CSU). He earned his Ph.D. at University of California, Santa Barbara in 2002. Dr. Zhao has a Bachelor of Science degree in Physics i
Xiongyi Liu is an Associate Professor in the Department of Curriculum and Foundations at Cleveland State University, USA. She obtained her Ph.D. in Educational Psychology from University of Nebraska, Lincoln, USA. Her research interests include technology
Dr. Chaomin Luo received his Ph.D. in Department of Electrical and Computer Engineering at University of Waterloo, Canada in 2008, where he was awarded Postgraduate Scholarship (PGS) from the Natural Sciences and Engineering Research Council (NSERC) of Canada; received the Best Student Paper Presentation Award at the SWORD’2007 Conference, earned his M.Sc. in Engineering Systems and Computing at University of Guelph, Canada, and his B.Eng. degree in Radio Engineering from Southeast University, China. He is currently an Associate Professor, Department of Electrical and Computer Engineering, at University of Detroit Mercy, Michigan, USA. He was awarded Faculty Research Awards in 2009, 2010, 2014, 2015, and 2016 at University of Detroit Mercy, Michigan, USA. His research interests include engineering education, robotics and automation, control, autonomous systems, computational intelligence and machine learning.
Dr. Luo was the General Co-Chair of the 1st IEEE International Workshop on Computational Intelligence in Smart Technologies (IEEE-CIST 2015), and Journal Special Issues Chair, IEEE 2016 International Conference on Smart Technologies (IEEE-SmarTech), USA. He was the Publicity Chair in the 2011 IEEE International Conference on Automation and Logistics. He was on the Conference Committee in the 2012 International Conference on Information and Automation and International Symposium on Biomedical Engineering and also the Publicity Chair in the 2012 IEEE International Conference on Automation and Logistics. Also, he was Chair and Vice Chair of IEEE SEM - Computational Intelligence Chapter and is currently a Chair of IEEE SEM - Computational Intelligence Chapter and Chair of Education Committee of IEEE SEM.
Dr. Luo serves as the Editorial Board Member of International Journal of Complex Systems – Computing, Sensing and Control; Associate Editor of International journal of Robotics and Automation (IJRA); and Associate Editor of International Journal of Swarm Intelligence Research (IJSIR). He has organized and chaired several special sessions on topics of Intelligent Vehicle Systems and Bio-inspired Intelligence in IEEE reputed international conferences such as IEEE-IJCNN, IEEE-SSCI, etc. He was the Panelist in the Department of Defense, USA, 2015-2016, 2016-2017 NDSEG Fellowship program, and National Science Foundation, USA, GRFP program, 2016-2017.
http://orcid.org/0000-0002-1929-8447
University of Science and Technology, Beijing, and Beijing Key Laboratory of Knowledge Engineering for Materials Science
[biography]
Xiong Luo received the Ph.D. degree from Central South University, China, in 2004. He currently works as a Professor in the School of Computer and Communication Engineering, University of Science and Technology Beijing, China. His current research interests include machine learning, cloud computing, and computational intelligence. He has published extensively in his areas of interest in journals, such as the Future Generation Computer Systems, Computer Networks, IEEE Access, and Personal and Ubiquitous Computing.
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