Overview
Our work in multimodal learning analytics focuses on the analysis of diverse educational data, including learner interactions, assessment outcomes, and engagement patterns within adaptive learning environments. The aim is to generate interpretable evidence that informs system design, evaluation, and continuous refinement across multiple projects and research stages.
This research area applies statistical and computational methods to examine learning processes across multiple modalities, with an emphasis on validity, transparency, and educational relevance. The resulting insights support data-informed decision-making and the development of adaptive, real-time learning systems.
Data Types and Analytical Methods
Data Types and Sources
Our analyses draw on multimodal educational data generated from digital learning environments, including:
- Learner interaction data (e.g., clickstreams, system logs)
- Assessment and performance data
- Engagement and behavioural indicators
- Learning artefacts and activity traces
These datasets provide a comprehensive basis for understanding learner behaviour and system performance in adaptive learning contexts.
Analytical Methods
We apply a range of qualitative, quantitative and computational methods, including:
- Statistical modelling and inference
- Learner modelling and User discovery
- Learning analytics techniques
- Pattern detection and exploratory analysis
- Predictive and descriptive modelling
All methods are selected based on research objectives, data characteristics, and the requirements of educational interpretation.
Research Application
Our analytical work supports:
- Design and refinement of adaptive learning systems
- Evaluation of learning effectiveness and engagement
- Investigation of learner behaviour and interaction patterns
- Evidence-based improvement of educational interventions
Output and Interpretation
We prioritise clarity, interpretability, and research relevance in all outputs. Findings are structured to support academic inquiry, system design decisions, and the development of adaptive educational technologies.
Confidentiality & Data Protection
We recognise that research data may include sensitive academic, institutional, and personal information. Our work is guided by established ethical standards, confidentiality obligations, and secure data governance practices. Data is handled with discretion and used only for defined, ethically approved research purposes.
We adhere to the following principles:
- confidentiality in all stages of data handling and analysis
- secure storage and controlled access to research data
- data sharing only where appropriate, and subject to explicit consent, ethical approval, and applicable agreements
- the use of Non-Disclosure Agreements (NDAs) where required
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