Using Learner Analytics to Explore the Potential Contribution of Multimodal Formative Assessment to Academic Success in Higher Education
Open accessAbstract
Blended learning pedagogical practices supported by learning management systems have become an important part of higher education curricula. In most cases, these blended curricula are evaluated through multimodal formative assessments. Although assessments can strongly affect student outcomes, research on the topic is limited. In this paper, we adopted a learning analytics approach to explore student engagement with formative assessments and the power of these assessments to predict student outcomes in an English for Academic Purposes courses in a Hong Kong university. The study retrieved the data logs from 7,815 students and used the data to analyze student engagement with the formative assessments. The results suggested that the students put effort into completing the assessments. The degree to which assessments predict learning outcomes depend on students’ level of subject knowledge and their understanding of the relevance of the assessments. This study showed that learning analytics provided reliable evidence for understanding students’ engagement and identifying at-risk students. Therefore, learning analytics research has the potential to inform pedagogical practice.
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Quizzes are useful in identifying at-risk students, but they are more helpful when students deem them relevant and useful for learning.
“Furthermore, quizzes are useful in identifying at-risk students, however they are more helpful when they are deemed relevant and useful for learning by students (e.g., Kohnke et al., 2022).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Kohnke et al., 2022
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Banihashem, S. K., Noroozi, O., van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. A. (2022) · Educational Research Review · doi:10.1016/j.edurev.2022.100489 · Cited by 246 · see citing works
Unpacking student engagement in higher education learning analytics: a systematic review
Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00493-y · Cited by 138 · see citing works
- Authors
- Kohnke, L., Foung, D., & Chen, J.
- Affiliations
- Education University of Hong Kong (HK)
- University of British Columbia (CA)
- Hong Kong Polytechnic University (HK)
- Corresponding author
- Foung, Dennis
- Venue
- Sage Open
- Publisher
- SAGE Publications
- Published
- 2022-04
- Volume
- 12
- Issue
- 2
- ISSN
- 2158-2440 (ISSN-L)
- OpenAlex
- W4223539203
- Cited by
- 13
- References
- 65
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 3 · 2023: 1 · 2022: 2
- FWCI
- 1.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- SAGE Open (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Education › Online and Blended Learning
- Keywords
- learning analytics, student engagement, academic success prediction, blended learning, virtual learning environments, English for academic purposes, at-risk students, student effort, higher education
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