Learning Analytics
Also known as: Educational Data Mining, Academic Analytics, Learning Data Analytics, Öğrenme Analitiği
Learning Analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, with the purpose of understanding and optimizing learning and the environments in which it occurs. Formally introduced by George Siemens and Phil Long in 2011, the approach draws on data generated in digital learning environments to provide educators, institutions, and learners with evidence-based feedback for improving educational outcomes.
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Method map
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When to use it
Learning analytics is appropriate when digital learning data are systematically logged — in LMS platforms, MOOCs, or blended courses — and when there is institutional capacity to act on insights. It is most effective for identifying at-risk students early in a course. Key assumptions include sufficient data volume, representative learner samples, and ethical data governance. It is less suited to purely face-to-face settings with no digital trace. Alternatives such as knowledge tracing are preferred when the goal is fine-grained skill mastery estimation rather than broad engagement monitoring.
Strengths & limitations
- Enables early identification of at-risk learners before dropout or failure occurs
- Provides scalable, data-driven feedback across large enrolments that human instructors cannot monitor individually
- Supports personalised learning paths by surfacing individual engagement and mastery patterns
- Generates institutional evidence for curriculum redesign and resource allocation decisions
- Requires substantial, high-quality event-log data that may not exist in all educational contexts
- Predictive models can encode historical biases, disadvantaging already marginalised student groups
- Correlation-based findings do not guarantee causal relationships between interventions and outcomes
- Implementation demands technical infrastructure and data literacy that many institutions lack
Frequently asked
How does learning analytics differ from educational data mining?
Educational data mining (EDM) focuses on developing new algorithms and discovery methods applied to educational datasets, emphasising technical novelty. Learning analytics, by contrast, prioritises the application of analytic techniques — including EDM methods — within real educational contexts to support decision-making by learners, instructors, and institutions. The two fields overlap substantially but differ in orientation: EDM is more method-driven, learning analytics more application-driven.
What ethical concerns arise with learning analytics?
Primary concerns include student privacy (who owns the data and how long it is retained), informed consent (whether students know they are being tracked), algorithmic fairness (whether models disadvantage certain demographic groups), and the risk of surveillance creep. Ethical frameworks such as the JISC Code of Practice recommend transparency, data minimisation, and student agency in how analytics are used and communicated.
What data volume is needed for reliable predictive models?
There is no universal threshold, but studies typically find that meaningful early-warning models require at least several hundred complete learner records with outcome labels. Model reliability also depends on feature quality, class balance, and validation strategy. In small cohorts, simpler descriptive dashboards and instructor judgement are often more trustworthy than black-box classifiers trained on insufficient data.
Sources
- Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40. link ↗
How to cite this page
ScholarGate. (2026, June 2). Learning Analytics. ScholarGate. https://scholargate.app/en/education-analytics/learning-analytics
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Knowledge Space TheoryEducation Analytics↔ compare
- Knowledge TracingEducation Analytics↔ compare
- Sequential Pattern MiningMachine learning↔ compare