Synergies of Learning Analytics and Learning Design: A Systematic Review of Student Outcomes
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The field of learning analytics (LA) has seen a gradual shift from purely data-driven approaches to more holistic views of improving student learning outcomes through data-informed learning design (LD). Despite the growing potential of LA in higher education (HE), the benefits are not yet convincing to the practitioner, in particular aspects of aligning LA data with LD toward desired learning outcomes. This review presents a systematic evaluation of effect sizes reported in 38 key studies in pursuit of effective LA approaches to measuring student learning gain for the enhancement of HE pedagogy and delivery. Large positive effects on student outcomes were found in LDs that fostered socio-collaborative and independent learning skills. Recent trends in personalization of learner feedback identified a need for the integration of student-idiosyncratic factors to improve the student experience and academic outcomes. Finally, key findings are developed into a new three-level framework, the LA Learning Gain Design (LALGD) model, to align meaningful data capture with pedagogical intentions and their learning outcomes. Suitable for various settings — face to face, blended, or fully online — the model contributes to data-informed learning and teaching pedagogies in HE.
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From Formative assessment and feedback in medical education: A practical guide: AMEE Guide No. 189 (2026) · cites this paper as [139]
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Enhancing Self-Regulated Learning With Artificial Intelligence-Powered Learning Analytics
Yildirim‐Erbasli, S. N., Gorgun, G., & Bulut, O. (2023) · Advances in early childhood and K-12 education · doi:10.4018/979-8-3693-0066-4.ch004 · Cited by 8 · see citing works
Hernández-Campos, M., Hilliger, I., & García-Peñalvo, F.-J. (2025) · Proceedings of the 15th International Learning Analytics and Knowledge Conference · doi:10.1145/3706468.3706518 · Cited by 4 · see citing works
“By addressing the gap in research between the intended purposes of LA for learning improvement and its actual impact [4], this work contributes to a deeper understanding of LA’s effectiveness in educational environments and actionable insights from LOs assessment [23].”
Online Learning During Covid-19 Pandemic, and Possibility of Adopting Computer-Based Test
Emdas, R., & Alruwaili, A. (2021) · International Journal of Computer Science and Information Technology · doi:10.5121/ijcsit.2021.13401 · Cited by 4 · see citing works
Singleton, R. S., Cosignani, D. R., Kam, M., Clune, M. L., Charlton, A., & Jowsey, T. (2023) · MedEdPublish · doi:10.12688/mep.19692.1 · Cited by 3 · see citing works
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Exploring the Impacts of Implementing Block Mode of Teaching in Higher Education
Trinh, N. T. T., Ghapanchi, A. H., & Purarjomandlangrudi, A. (2024) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v19i06.49229 · Cited by 2 · see citing works
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A Framework to Assist in Didactic Planning at Undergraduate Level
Hernández-Carrasco, D. A., Gómez, C. E. R., González‐López, S., López‐López, A., García-Gorrostieta, J. M., & Borrego, G. (2022) · Mathematics · doi:10.3390/math10091355 · Cited by 2 · see citing works
Percepción de los conocimientos de la analítica del aprendizaje en la educación superior
de Jesús, M., Figueroa-Garza, F. G., & Ramírez-Ramírez, J. F. (2023) · Vinculatégica EFAN · doi:10.29105/vtga9.5-506 · Cited by 1 · see citing works
The Use of AI in Teaching and Learning
Kaiser, G., & Kaiser, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2397-8.ch010 · Cited by 1 · see citing works
Munim, Z. H., Kjeldsberg, F., Kim, T.-E., & Bustgaard, M. (2025) · Array · doi:10.1016/j.array.2025.100489 · Cited by 1 · see citing works
How Late is Too Late? Clustering Student Timing Behaviors to Predict Academic Success
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Hernández-Campos, M., Hilliger, I., & García‐Peñalvo, F. J. (2026) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3785022.3785072 · Cited by 1 · see citing works
García-García, F. J., Gómez‐Núñez, M. I., & Molla‐Esparza, C. (2026) · Higher Education · doi:10.1007/s10734-026-01709-y · Cited by 3 · see citing works
Tu, C., Peterson, P. C., Yen, C., Harati, H., Shelton, C. C., & Sujo-Montes, L. E. (2023) · Educational Media International · doi:10.1080/09523987.2023.2324583 · Cited by 1 · see citing works
Computer Science & Information Technology (CS & IT)
(2021) · doi:10.5121/csit.2021.1108 · Cited by 1 · see citing works
What are the Aspects of Adopting Computer-Based Exams and do they Impact Negatively on Students?
Emdas, R., & Alruwaili, A. (2021) · doi:10.5121/csit.2021.110808
Lūsēna-Ezera, I., Siliņa-Jasjukeviča, G., Līduma, D., & Kaulēns, O. (2023) · International Conference on Education Technology and Computer · doi:10.1145/3629296.3629352
LEARNING ANALYTICS FOR PERSONAL LEARNING ENVIRONMENTS: DETERMINING JOURNAL PUBLICATION TRENDS
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Where Is the Teacher in Data Analytics in Education?
Nguyen, N. B. C., & Karunaratne, T. (2024) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v19i06.50165
Optimizing Global Learning Programs Through Learner Analytics
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A knowledge database discovery approach for improving quality in higher education institutions
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A systematic literature review on the definition and core components of learning analytics systems
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Enhancing Learning Design Decisions with Risk Management Strategies
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Smart hybrid models for personalized learning in higher education
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- Authors
- Blumenstein, M.
- Affiliations
- University of Auckland (NZ)
- Corresponding author
- Blumenstein, Marion
- ORCID
- Blumenstein, Marion
- Venue
- Journal of Learning Analytics
- Published
- 2020-12-17
- Volume
- 7
- Issue
- 3
- Pages
- 13-32
- ISSN
- 1929-7750 (ISSN-L)
- OpenAlex
- W3111465890
- Cited by
- 54
- References
- 88
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 5 · 2023: 13 · 2022: 7 · 2021: 7
- FWCI
- 1.16
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Journal of Learning Analytics (publishedVersion, cc-by-nc-nd)
- ResearchSpace (University of Auckland) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Evaluation of Teaching Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- learning analytics, learning design, learning outcomes, university teaching, data-driven instruction, learning gains, cooperative learning, independent learning skills, effect size
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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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