New Perspectives in Science Education
Assessment of Student Learning after Pandemic COVID-19
Mohammad Shokrolah Shirazi, R.B. Annis School of Engineering, University of Indianapolis (United States)
Megan Hammond, R.B. Annis School of Engineering, University of Indianapolis (United States)
Kenneth Reid, R.B. Annis School of Engineering, University of Indianapolis (United States)
Abstract
In this research, we evaluate student learning regarding course level outcomes (CLO) [1] and performance indicators for computer science and industrial engineering courses taught by the same instructors, before and during the COVID-19 pandemic. Two computer science courses, Computer
Architecture and Parallel Processing (CSCI 230), Operating Systems (SWEN 310), and one industrial engineering course, Network Analysis and Strategies (ISEN 430) were taught in Fall 2019 and Fall 2020 as face-to-face and fully online, respectively. Multiple instructional changes were applied to the courses during the pandemic, since students were moved to a fully online platform. In the 2019 offerings of CSCI courses, students accessed Raspberry Pi clusters to practice the ARM assembly,
and parallel coding with multi-threads through the university network, not accessible while off-campus. During the pandemic, we developed an ARM-based system through amazon web services [2] allowing students to access the clusters from everywhere. The similar platform using Linux-based instances was dedicated for the Operating System course. In 2019, ISEN 430 utilized face-to-face lecture and workshop components to address student programming and analysis issues with one-on-one assistance from the instructor and promote student collaboration. The transition to fully online removed the workshop component of the course and limited lectures to once a week via Zoom. Students utilized the AWS to access analysis software off-campus and Zoom meetings provided platforms to share screens and collaborate virtually on the course projects. In addition to updating the software and hardware tools, lectures and software walk-throughs were recorded and posted to the online University teaching platform called ACE, powered by Sakai, allowing students the opportunity to
review previous class material for reinforcement learning. We grouped similar assignments designed to assess the CLOs in all three courses to compare and evaluate student performance in the 2019 and 2020 semesters using two-sample t-tests. For SWEN 310 with 6 ClOs, we observed improvement in two of them and in CSCI 230, with 4 CLOs the improvement occurred for three of them during the pandemic. The t-test didn’t show a significant difference even for their final grade except coding
assignments performed on the online platform. For ISEN 430, 3 out of the 5 CLOs presented overall improvement during the online format, but no statistically significant difference in the scores was calculated.
Keywords: Course Learning Outcome, ARM Assembly, Amazon web services.
References:
- Trigwell, K., Prosser, M. Improving the quality of student learning: the influence of learning context and student approaches to learning on learning outcomes. High Educ 22, 251–266 (1991).
- J. C. Nwokeji et al., "Panel: Incorporating Cloud Computing Competences into Computing Curriculum: Challenges & Prospects," 2020 IEEE Frontiers in Education Conference (FIE), Uppsala, 2020, pp. 1-3.
