An Investigation on Assessment Strategies, Student Engagement, and Retention for Large Cohorts Affected by COVID Learning Disruptions

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Chin, Jeannette ORCID: https://orcid.org/0000-0002-9398-5579 (2024) An Investigation on Assessment Strategies, Student Engagement, and Retention for Large Cohorts Affected by COVID Learning Disruptions. In: Proceedings of the 3rd International Conference on Innovations in Computing Research (ICR’24). Lecture Notes in Networks and Systems . Springer, 237–248. ISBN 978-3-031-65521-0

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Abstract

This paper reports the results of an investigation of assessment strategies for student learning, engagement, and retention of an undergraduate year 1 module in the Computing Science discipline at the University of East Anglia, UK. In this study, three different assessment methods were considered, one for each year, over a three-year period (2020-21 to 2022-23). The study period coincided with the COVID pandemic where the cohorts had their secondary school learning disrupted one way or another, prior to embarking on their university career. The assessment methods investigated did not cover all of the learning objectives, however the learning objectives assessed were comparable with one another. The results show that the presentation and in-class test assessment methods achieved normal distributions of marks, while the marks for the practice-based portfolio assessment were negatively skewed, suggesting the nature of the assessment requires more balancing tasks. Further, student attendance and submission rate were found to have been influenced by the assessment type students had to undertake. Cohorts who undertook the practice-based portfolio assessment had better student engagement and submission rate, at 73% and 91.92% respectively. Finally, learning disruption caused by the COVID pandemic was found to be correlated with student retention, where cohorts whose grades were determined solely by their teacher prior to attending university had a 24% higher chance of withdrawing from the course or transferring to a different course compared to those whose grades were determined by exams.

Item Type:	Book Section
Uncontrolled Keywords:	assessment method,covid pandemic,computing science,large cohort,student engagement,student retention,control and systems engineering,signal processing,computer networks and communications ,/dk/atira/pure/subjectarea/asjc/2200/2207
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	07 Nov 2024 16:30
Last Modified:	18 Dec 2024 01:12
URI:	https://ueaeprints.uea.ac.uk/id/eprint/97572
DOI:	10.1007/978-3-031-65522-7_22

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