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Student Perceptions of the Complete Online Transition of Two CS Courses in Response to the COVID-19 Pandemic
2021 ASEE Virtual Annual Conference, ASEE 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1696060
ABSTRACT
In this evidence-based practice paper, we present results from surveys of students in two CS courses offered in Spring 2020 at Virginia Tech, a large, public research university a programming-intensive CS2-level course and an upper division theory course, Formal Languages and Automata. Spring 2020 was extraordinary as a result of the COVID-19 pandemic. Universities in the US and across the globe switched to a complete online delivery mode instead of the traditional face-to-face mode. This was challenging to both educators and students, as the transition took place on short notice in the middle of the Spring 2020 semester. We were interested to know those course components students perceived as most beneficial to their learning, before and then after the online transition, and their mode preferences for each regarding online vs. Face-to-Face. By comparing student reactions across courses, we gain insights on which components are easily adapted to online delivery, and which require further innovation. COVID was unfortunate, but gave a rare opportunity to compare students' reflections on F2F instruction with online instructional materials for half of a semester vs. entirely online delivery of the same course during the second half. Although the instruction provided during the second half of the semester may not be the same as what would have been provided had the course been designed as a fully online course from the beginning, it did provide the opportunity for us to acquire insights for future instruction. Results indicated that some course components were perceived to be more useful either before or after the transition, and preferences were not the same for the two courses. Furthermore, to determine what course components need further improvement before transitioning to fully online mode, we computed a logistic regression model. Results indicated that for each course, different course components both before and after the transition significantly affected students' preference of course modality. © American Society for Engineering Education, 2021
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Collection: Databases of international organizations Database: Scopus Language: English Journal: 2021 ASEE Virtual Annual Conference, ASEE 2021 Year: 2021 Document Type: Article

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Collection: Databases of international organizations Database: Scopus Language: English Journal: 2021 ASEE Virtual Annual Conference, ASEE 2021 Year: 2021 Document Type: Article