Asee peer logo

Effect of Active Learning on Students' Performance in Remote ECE Classes with Lab Sections

Download Paper |

Conference

2022 ASEE Annual Conference & Exposition

Location

Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

ECE Division Technical Session 1: Online or Remote Teaching and Curricular Developments

Page Count

12

DOI

10.18260/1-2--40730

Permanent URL

https://peer.asee.org/40730

Download Count

300

Request a correction

Paper Authors

biography

Ahmed Dallal University of Pittsburgh

visit author page

Dr. Dallal is an assistant professor at the department of electrical and computer engineering, Unversity of Pittsburgh, since August 2017. Dr. Dallal's primary focus is on education development and innovation. His research interests include biomedical signal processing, biomedical image analysis, computer vision, machine learning, networked control systems, and human-machine learning. Dr. Dallal's pedagogy and engineering research interests are on active learning, flipped classroom, problem-based learning, and collaborative learning.

visit author page

biography

Mohamed Zaghloul

visit author page

Mohamed A. S. Zaghloul was born in Cairo, Egypt, in 1987. He received his B.E. degree in Electronics and Electrical Communications Engineering in 2009, and his M.Sc. degree in Engineering Physics in 2012, both from the Faculty of Engineering at Cairo University. In 2019, he received a Ph.D. from the Electrical and Computer Engineering department of the University of Pittsburgh, in developing optical fiber sensors for monitoring harsh environments. Since 2019, he has been appointed as an Assistant Professor in the same department of the same school. Zaghloul is a recipient of multiple research and teaching awards; he currently holds the John C. Mascaro Faculty Scholarship in Sustainability.

visit author page

biography

Amr Hassan University of Pittsburgh

visit author page

Dr. Amr Hassan (also know as Amr Mahmoud) received his B.Sc. degree in Electronics and Electrical Communications Engineering and the M.Sc degree in Engineering Physics from Cairo University, Egypt, in 2011 and 2015, respectively. He earned his PhD in Computer Engineering from the Electrical and Computer Engineering Department at the University of Pittsburgh, USA. Currently, he is an Assistant Professor with the same department, since August 2019. Dr. Hassan's primary focus is on education development and innovation. His Research interests include, but not limited to: Machine Learning, especially Deep Learning, for Image Processing and Video Prediction, Neuromorphic Computing Systems and its applications.

visit author page

Download Paper |

Abstract

To mitigate the spread of COVID-19, universities transition to remote instructions. However, this new mode of instruction introduced challenges to instructors and students regarding lecture delivery methods and class interactivity. Research conducted before the pandemic showed that active learning efficiently increases student learning, engagement, and interest in the topics being taught. Thus, it seems that active learning could remedy the concerns related to class interactions in remote classrooms. However, there is a lack of research on the effectiveness of active learning in remote class setups. This study investigates the effectiveness of active learning techniques in enhancing student performance and attendance rates in remote electrical and computer engineering courses with a lab section. For the stated objective, the study considers two classes covering electronic circuit design and feedback control over the summer semester of 2021. To study the effect of active learning on students' performance, we used only a few active learning exercises during the first half of the semester. In contrast, more involved active learning approaches were adopted during the second half of the semester. Thus, we could compare the students' performance in the modules with few active learning in the first half of the semester vs. their performance in the modules where more active learning exercises were used during the second half of the semester. To quantitatively measure the effectiveness of the adopted active learning approaches, we compared the lab, homework, quizzes, and test scores from the modules studied in the first half of the semester to those from the second half of the semester where more involved approaches were used. The analysis showed a slight enhancement in the students' scores in the modules with active learning exercises. Besides the exam scores, we used online surveys to gather the students' perceptions of active learning. Content analyses of survey responses suggest that active learning in a remote setup is well perceived and helps the class attendance rate, despite some logistic challenges with the hardware lab component.

Dallal, A., & Zaghloul, M., & Hassan, A. (2022, August), Effect of Active Learning on Students' Performance in Remote ECE Classes with Lab Sections Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--40730

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2022 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015