Learning Analytics

Overview

This course grounds students in the theory, methods, and practice of learning analytics — the measurement, collection, analysis, and reporting of data about learners and their contexts. Students learn to understand, develop, and apply learning analytics techniques to real educational data, gaining both conceptual understanding and practical skills.

What You Will Learn

The course covers the full learning analytics pipeline: from data collection and ethical considerations, through data cleaning and exploration, to advanced analytical methods including predictive modeling, network analysis, clustering, and visualization. Students work with R throughout the course, building a practical toolkit they can apply to their own research or professional contexts.

Format

The course uses a blend of lectures, hands-on labs, collaborative group projects, and individual assignments. Assessment is entirely through coursework — practical exercises, a data analysis project, and critical literature review. Every student receives detailed individualized feedback on each assignment. Collaborative learning and peer feedback are integral components.

Who Should Take This Course

The course is open to all PhD students registered at Finnish universities and all students at the University of Eastern Finland. It is designed for students from any discipline who want to understand how educational data can be used to improve learning and teaching. No prior experience with R or data analysis is required.