Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates
Abstract
Many researchers who study the impact of computer-based assessment (CBA) focus on the affordances or complexities of CBA approaches in comparison to traditional assessment methods. This study examines how CBA approaches were configured within and between modules, and the impact of assessment design on students' engagement, satisfaction, and pass rates. The analysis was conducted using a combination of longitudinal visualisations, correlational analysis, and fixed-effect models on 74 undergraduate modules and their 72,377 students. Our findings indicate that educators designed very different assessment strategies, which significantly influenced student engagement as measured by time spent in the virtual learning environment (VLE). Weekly analyses indicated that assessment activities were balanced with other learning activities , which suggests that educators tended to aim for a consistent workload when designing assessment strategies. Since most of the assessments were computer-based, students spent more time on the VLE during assessment weeks. By controlling for heterogeneity within and between modules, learning design could explain up to 69% of the variability in students' time spent on the VLE. Furthermore, assessment activities were significantly related to pass rates, but no clear relation with satisfaction was found. Our findings highlight the importance of CBA and learning design to how students learn online.
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Engagement, satisfaction, and pass rates of students in the learning environment are not just greatly influenced by learning design but are especially manipulated by how instructors stabilize their learning design activities in the course modules on weekly basis.
“Findings indicated that engagement, satisfaction, and pass rates of students in the learning environment are not just greatly influenced by learning design but are especially manipulated by how instructors stabilize their learning design activities in the course modules on weekly basis.”
From Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education (2022) · cites this paper as [125]
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- Authors
- Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., & Whitelock, D.
- Affiliations
- The Open University (GB)
- Corresponding author
- Rienties, Bart
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 76
- Pages
- 703-714
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2595316996
- Cited by
- 147
- References
- 48
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 10 · 2023: 16 · 2022: 23 · 2021: 18 · 2020: 20 · 2019: 21 · 2018: 16 · 2017: 4
- FWCI
- 59.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers in Human Behavior (publishedVersion)
- Open Research Online - ORO (The Open University) (acceptedVersion)
- Funders
- Leverhulme Trust Research
- Topics
- Social Sciences › Education › Online and Blended Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- computer-based testing, assessment design, student engagement, student satisfaction, pass rates, virtual learning environments, learning design, online learning, workload balance, assessment activities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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