Resources
A searchable library of foundational concepts, frameworks and references across learning analytics and instructional design.
What is Learning Analytics?
The measurement, collection, analysis and reporting of data about learners and their contexts to understand and optimise learning.
Merrill's First Principles of Instruction
Five research-based principles: task-centred, activation, demonstration, application and integration.
Kirkpatrick's four levels of evaluation
Reaction, Learning, Behaviour and Results — a foundational framework for evaluating training effectiveness.
The science of learning
Evidence-based principles including retrieval practice, spaced repetition, interleaving, elaboration and dual coding.
Cognitive Load Theory (Sweller)
Distinguishes intrinsic, extraneous and germane load. Instructional design should minimise extraneous load and support germane processing.
Effective learning dashboards
Best practices for visualising learning data: choose the right chart, reduce chart-junk, emphasise comparisons over decoration.
From data to actionable insight
Combining data engineering, statistics and pedagogy to answer questions about learning at scale.
Ethics in learning analytics
Considerations around consent, transparency, data minimisation and avoiding algorithmic bias.
ADDIE and agile alternatives
The classic Analyse-Design-Develop-Implement-Evaluate model and modern iterative variants (SAM, Lean ID).