A Hybrid Pedagogy through Topical Guide Objective to Enhance Student Learning in MIPS Instruction Set Design
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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July 12, 2024
- Conference Session
- Tagged Division
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Computers in Education Division (COED)
- Tagged Topic
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Diversity
- Permanent URL
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https://peer.asee.org/46448
Paper Authors
Timothy Sellers
Mississippi State University
orcid.org/0000-0001-8344-9804
Timothy Sellers received the B.S. degree in robotics and automation technology and applied science in electro-mechanical engineering from the Alcorn State University, Lorman, MS, USA in 2020. He is currently pursuing a Ph.D. degree in the Department of Electrical and Computer Engineering at Mississippi State University, Mississippi State, MS, USA. He is currently a Graduate Teaching Assistant for Senior Design II (ECE4542/ECE4522) and was for Advance Circuits (ECE3434) at the undergraduate level and as guest lecturer delivered graduate-level courses, Advanced Robotics (ECE 8743) and Computational Intelligence (ECE 8833). He received the ECE Outstanding Teaching Assistant Award from the Department of Electrical and Computer Engineering, Mississippi State University in 2021. He received the Research Travel Award from Bagley College of Engineering, Mississippi State University in 2024. He has also received the Bagley College of Engineering Student Hall of Fame award in 2024. He won three poster presentation awards at multiple conferences. Mr. Sellers has served on the technical program committee for numerous international conferences and journals, such as IJMLC, ICSI, and PRIS, etc. He has extensively published journal and conference papers in engineering education and robotics fields. His research interests include engineering education, robotics and autonomous systems, human robot interaction, deep learning, and computational intelligence.
Tingjun Lei Mississippi State University
Dr. Tingjun Lei is currently a Postdoctoral Research Fellow in the Department of Electrical and Computer Engineering at the Mississippi State University (MSU). He received his Ph.D. degree in electrical and computer engineering with the Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA., in 2023, his M.S. degree in electrical and computer engineering from the New York Institute of Technology, Old Westbury, NY, USA, in 2016, and the B.S. degree in intelligent transportation engineering from Shanghai Maritime University, Shanghai, China, in 2014. He was Graduate Teaching Assistant for ECE1013 Foundations in ECE, ECE1022 Foundations in Design, ECE4713/6713 Computer Architecture, and ECE4753/6753 Introduction to Robotics at the undergraduate level and as a guest lecturer delivered graduate-level courses, ECE 8743 Advanced Robotics and ECE8833 Computational Intelligence. He received the ECE Best Graduate Researcher Award from the Department of Electrical and Computer Engineering, Mississippi State University in 2023. He received the Research Travel Award from Bagley College of Engineering, Mississippi State University in 2023. His two papers have been selected and featured as cover articles on Intelligence & Robotics Journal. He won six oral and poster presentation awards at multiple conferences. Dr. Lei received the Best Paper Award in 2022 International Conference on Swarm Intelligence. Dr. Lei serves as Youth Editorial Board Member of Intelligence and Robotics. Dr. Lei has served on the technical program committee for numerous international conferences, such as IEEE-CEC, IEEE-IJCNN, ICSI, and PRIS, etc. Dr. Lei has extensively published journal and conference papers in robotics, intelligent systems, and engineering education areas. His research interests include engineering education, robotics and autonomous systems, human robot interaction, deep learning, intelligent transportation systems, and evolutionary computation.
Chaomin Luo
Mississippi State University
orcid.org/0000-0002-7578-3631
Dr. Chaomin Luo (Senior Member, IEEE) holds a Ph.D. degree in electrical and computer engineering from the Department of Electrical and Computer Engineering at the University of Waterloo, Canada in 2008. He also earned an M.Sc. degree in engineering systems and computing from the University of Guelph, Canada in 2002, and a B.Sc. in electrical engineering from Southeast University. Currently, he is an Associate Professor in the Department of Electrical and Computer Engineering at Mississippi State University. His research interests include engineering education, intelligent systems, control and automation, robotics, and autonomous systems. He is Associate Editor in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019). He is Tutorials Co-Chair in the 2020 IEEE Symposium Series on Computational Intelligence. Dr. Luo was the recipient of the Best Paper Awards in IEEE International Conference on Information and Automation, International Conference on Swarm Intelligence, and SWORD Conference. His research interests include Robotics, Autonomous Systems, and Control and Automation. Dr. Luo is an IEEE senior member, INFORMS, and ASEE member. Dr. Luo is active nationally and internationally in his research field. He was the Program Co-Chair in 2018 IEEE International Conference on Information and Automation (IEEE-ICIA’2018). He was the Plenary Session Co-Chair in the 2021 and 2019 International Conference on Swarm Intelligence, and he was the Invited Session Co-Chair in the 2017 International Conference on Swarm Intelligence. He 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), Cleveland, OH, USA. He was Chair and Vice Chair of IEEE SEM - Computational Intelligence Chapter and was a Chair of IEEE SEM - Computational Intelligence Chapter and Chair of Education Committee of IEEE SEM. He has organized and chaired several special sessions on topics of Intelligent Vehicle Systems and Bio-inspired Intelligence in reputed international conferences such as IJCNN, IEEE-SSCI, IEEE-CEC, IEEE-CASE, and IEEE-Fuzzy, etc. He has extensively published in reputed journals and conference proceedings, such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on SMC, IEEE Transactions on Cybernetics, IEEE-ICRA, and IEEE-IROS, etc. Dr. Luo serves as Associate Editor of IEEE Transactions on Cognitive and Developmental Systems, International journal of Robotics and Automation, and Associate Editor of International Journal of Swarm Intelligence Research (IJSIR).
Gene Eu Jan
Gene Eu Jan (M’00) received the B.S. degree in electrical engineering from National Taiwan University, Taipei, Taiwan, in 1982 and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Maryland, College Park, MD, USA, in 1988 and 1992, respectively.
He has been a Professor with the Departments of Computer Science and Electrical Engineering, National Taipei University, New Taipei City, Taiwan since 2004, where he also served as the Dean of the College of Electrical Engineering and Computer Science from 2007 to 2009. Currently, he is the president of Tainan National University of the Arts. He has published more than 270 articles related to parallel
computer systems, interconnection networks, path planning, electronic design automation,
and VLSI systems design in journals, conference proceedings, and books.
Zhuming Bi
Purdue University, Fort Wayne
orcid.org/0000-0002-8145-7883
Zhuming Bi (Senior Member, IEEE) received the Ph.D. degree from the Harbin Institute of Technology, Harbin, China, in 1994, and the Ph.D. degree from the University of Saskatchewan, Saskatoon, SK, Canada, in 2002. He has international work experience in Mainland China, Hong Kong, Singapore, Canada, UK, Finland, and USA. He is currently a professor of Mechanical Engineering with Purdue University Fort Wayne, Fort Wayne, IN, USA. His current research interests include robotics, mechatronics, Internet of Things (IoT), digital manufacturing, automatic robotic processing, and enterprise information systems. He has published 6 research books and over 180 journal publications in these fields.
Abstract
The Computer Architecture curriculum primarily delves into the intricacies of modern microprocessor and computer system architecture design. One of the most challenging aspects of the curriculum is the study of MIPS instruction set design. In this paper, we share our recent experiences in applying an integrated pedagogical approach to this subject. Our methodology utilizes a hybrid combination of techniques including Topical Guide Objective (TGO) method, on-going exercises, and classroom demonstration methods, all while considering the assessment of ABET criteria in our course structure. Topical Guide Objectives (TGOs) define specific learning objectives for each topic in MIPS instruction set design in the computer architecture curriculum. By having clearly defined objectives, students have a better understanding of what they need to learn, making their learning goals more tangible. This paper specifically focuses on the use of educational exercises tailored for computer architecture students, aimed at deepening their comprehension of MIPS instruction set design and related principles. The hybrid method denotes the application of educational models that cater to both qualitative and quantitative aspects of MIPS instruction set design. A sequence of on-going exercises, in-class activities and homework assignments were designed and incorporated into this hybrid model to facilitate a deeper understanding of instruction set design for students. These assignments are explicitly aligned with the TGOs covered in our lectures. Each TGO comprises a learning objective, a set of key points and basic concepts, their interrelation, and one or more exercise problems. The TGO method primarily consists of two components as follows. Following this approach, students are encouraged to complete homework assignments, engage in ongoing exercises, and participate in classroom activities, which incorporate the two elements of TGO: topical guide objectives for students to study and example problems for students to solve. TGO is used to align assessments, such as assignments, homework assignments, and exams, with the learning objectives. This ensures that the assessments accurately measure whether students have achieved the intended learning outcomes. We anticipate that these carefully designed ongoing exercises, in-class activities, and class demonstrations will enhance students' attitudes and foster more active and meaningful participation in their learning process. TGO associated with on-going exercises and classroom demonstration method provides a basis for measuring the effectiveness of the educational program in this class. By assessing whether students meet the defined objectives, instructors and our computer engineering program may evaluate the success of their teaching methods in the computer architecture through MIPS instruction set design. The results of self-assessment, course evaluations, ABET-enabled assessments, and exams affirm that this integrated pedagogical approach can boost student motivation and improve their ability to grasp computer architecture, yielding satisfactory outcomes for both instructors and students.
Sellers, T., & Lei, T., & Luo, C., & Jan, G. E., & Bi, Z. (2024, June), A Hybrid Pedagogy through Topical Guide Objective to Enhance Student Learning in MIPS Instruction Set Design Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/46448
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