This subtopic covers Data Science which plays a central role in learning engineering by providing methodologies for collecting, analyzing, and interpreting data from learning environments. Key applications include:
Data Collection and Instrumentation: Developing systems to capture learner interactions and performance data.
Learning Analytics and Insights: Using analytics to identify patterns, detect learner and environmental states, predict outcomes, and evaluate efficacy of learning and learning solution variables.
Adaptive Algorithms: Creating models that adapt to individual learners and groups of learners in real-time, personalizing content, and feedback.