Summary:

This interactive workshop aims to introduce participants to practical tools and techniques used in learning analytics. Participants will explore real or simulated educational datasets and learn how to visualize student engagement, performance, and retention patterns using open-source tools such as Jupyter Notebooks with Python or R. The session emphasizes hands-on activities, including building dashboards and experimenting with basic predictive models. It’s designed for educators, instructional designers, or researchers who want to move from theory to practical application.

Key Outcomes:

• Understand different types of educational data
• Gain experience using analytics tools to explore and visualize learning data
• Create simple dashboards or predictive models for learner insights

Materials:

1. Learning Analytics in Higher Education (an brief graphical summary)
2. Discover how instructional design choices enable learning analytics (activity)
3. The privacy spectrum: Analytics for monitoring, not surveillance (reflection)
4. Open learner model dashboard (visualize learner progress across broad and detailed programming concepts.)
5. Open learner dashboards: How users view and interact? (after checking material 4, please take a few minutes to complete our survey.) 
6. Teacher-facing dashboards: A hands-on demo
7. Simulated data to test the teacher dashboard demo (dataset_1, dataset_2)

Schedule:

• 10:00 – 10:30: Welcome and introduction
• 10:30 – 12:00: Student-facing Learning Analytics
• 12:00 – 13:00: Lunch Break
• 13:00 – 14:30: Teacher-facing Learning Analytics
• 14:30 – 14:45: Short Break
• 14:45 – 16:00: Learning Analytics, Privacy and Ethics
• 16:00 – 17:00: Open Discussion and Conclusion