Faculty Adoption Over Time to Online Laboratories in Electrical and Computer Engineering Classes Throughout COVID-19

This work-in-progress innovative practice paper presents research that follows up on a recent paper on faculty perspectives regarding the transition to exclusively online laboratory classes in electrical and computer engineering, which was presented at a conference in 2022. As we gradually moved away from the COVID-19 pandemic, faculty phased back to using in-person labs instead of the online (remote and virtual) laboratories that they had transitioned to in 2020. The data presented in this paper comes from two separate sets of interviews, each lasting 30 to 45 minutes, with five interviews conducted in each set. These interviews were recorded within two years of each other. Similar to the previous paper, we once again apply the diffusion of innovation and the framework of propagation to identify effectiveness and fit as key factors in the successful adoption of innovation. The online labs utilized in this study include the VISIR remote lab, the EMONA remote lab, and the MULTISIM online circuit simulator. The objective of this paper is to explore any mindset changes that faculty members have experienced regarding the adoption of these online laboratories in their classrooms. This paper allows us to gain deeper insight into important aspects related to the integration of online laboratories into our classrooms. Common themes from the first set of five interviews include constant comparison to “real” labs, time flexibility and individualized support, differences in learning outcomes, the necessary connection between lecture and lab, and student engagement with the lab. Preliminary results from the follow-up set of interviews show a similar set of results but with a stronger emphasis on the comfort found in using online labs after a year of use. There is a significant push to use the remote labs in conjunction with the in-person labs or even as a pre-lab. Through this work, we aim to develop an evidence-based understanding using an inductive approach to the data.