Minneapolis, MN
August 23, 2022
June 26, 2022
June 29, 2022
12
10.18260/1-2--41421
https://peer.asee.org/41421
421
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.
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.
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.
During the 2020/2021 academic year, most of the US universities switched fully to remote learning, as a consequence of the world’s battle against COVID-19. It was not until Fall 2021, and with most of the faculty and student body being vaccinated, when life started to get back to normal and most universities opened their doors back to in-person classrooms. Still, some universities had to partially switch back remote classrooms when their on-campus COVID-19 cases spiked. It seems like the remote classroom is not vanishing anytime soon, and it’s essential to make sure that students get the best experience that a remote learning classroom can offer.
Previous research, that was conducted during the first year of the pandemic, showed that one of the main points that students suffer from is maintaining their engagement level in an online environment. It’s hard for them to keep high levels of focus and engagement when they are just behind a computer screen and not physically present with their professor and classmates. Another problem that contributes directly is all the sources of distraction that they were surrounded with, wherever the students were attending their remote classrooms from.
In this work, we present a comprehensive study on the effectiveness of different active learning practices that can be applied in remote classrooms. The study considers three courses offered during the summer semesters of 2020 and 2021, all of which were delivered remotely in a synchronous fashion. The courses span the sophomore and junior years of the Electrical and Computer Engineering Department of the school hosting this study and cover a broad spectrum of types, including two lab-based courses and one regular-book course. For the regular book course, the students’ performance in exams and assignments was compared between Summer 2020, where no active learning practices were applied, and Summer 2021, where active learning techniques were used. For the two lab-based courses, students’ performance in lab and test scores from the first half of Summer 2021, where minimal active learning practices were applied, will be compared to those in the second half of the semester, where more active learning techniques were used. In addition to the quantitative study of students’ scores, the paper presents a qualitative study based on an analysis of student surveys. Eighty-three students were surveyed to capture their opinions on the adopted active learning practices in remote classrooms. Both sets of results will be analyzed, and the best practices that can be effectively applied in a remote classroom will be proposed.
Hassan, A., & Dallal, A., & Zaghloul, M. (2022, August), A Comprehensive Study on The Effectiveness of Active Learning Techniques in Remote Learning Classes Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41421
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