Data Informed Teaching Practices with Learning Analytics

Event Date & Time

Event Description

In-person

This event is part of the CTLT Spring Institute, taking place from June 3–6, 2024.

This workshop aims to introduce Learning Analytics (LA) concepts, showcase relevant UBC data sources for teaching and learning (with a focus on Canvas-related data), and facilitate hands-on exploration of these data sources with the use of Jupyter notebooks (no prior experience necessary). Data-informed decisions can help your teaching practice by providing evidence to guide changes in your practice and offer an avenue to evaluate those changes. For instance, if you have content that you believe students should be engaging with early and often, learning analytics could provide a method of examining if your intuition aligns with students’ behavior.

We will begin with an overview of Learning Analytics and an open discussion about how learning analytics might fit into the participants’ current practices. As an interdisciplinary field, voices from diverse roles in education are important in this discussion.

“As a research and teaching field, Learning Analytics sits at the convergence of Learning (e.g. educational research, learning and assessment sciences, educational technology), Analytics (e.g. statistics, visualization, computer/data sciences, artificial intelligence), and Human-Centered Design (e.g. usability, participatory design, sociotechnical systems thinking)” – Society for Learning Analytics Research, https://www.solaresearch.org/about/what-is-learning-analytics/ .

We will then work with Jupyter Notebooks that will be developed to “guide” participants through data cleaning, exploration, and visualizations. The session will conclude by reinforcing connections between the practical data work and our previous discussion on learning analytics, highlighting ways participants can incorporate both into their work effectively.

The learning outcomes for all participants will be, to:

• Gain an understanding of learning analytics and its potential applications to enhance teaching practices, including familiarity with UBC’s available data sources
• Develop a foundational understanding of basic principles in data analysis and visualization
• Acquire introductory knowledge of Jupyter notebooks and their practical applications for data analysis and exploration.

 

Facilitators:

• Alison Myers, Programmer Analyst, LTHub Learning Analytics Team, CTLT
• Craig Thompson, Programmer Analyst, LTHub Learning Analytics Team, CTLT