Data-Informed Design

Wise & Jung (2019) present an Initial Model of Analytics Use:

Diagram with 2 boxes. First box reads Sensemaking with boxes and arrows for Areas of Curiosity, Question generation, Interpreting Data: Read Data - Get Oriented through Focused Attention and Find Absolute and Relative Reference Points and also Explain Pattern - Triangulate, Contextualize, Make Attribution, generally Affective Processes. Second box reads Pedagogical Response with boxes and arrows for Take Action - Whole-Class Scaffolding, Targeted Scaffolding, Revise Course Design, and Wait-and-See, and Reflect on Pedagogy, then Check Impact

Building on this model, the video below explores a framework for data-informed design in teaching and learning environments.

Featuring Laurie Harrison – Director, Digital Learning Innovation, University of Toronto

Generating and Workshopping Questions

When working with data, it’s important to critically examine our questions, consider our assumptions and expectations of the data, and anticipate challenges, opportunities, and questions related to our areas of curiosity. The interactive slides below aim to capture some of this process. Groups of participants were given questions related to teaching and learning, and workshopped them together. Some of the dialogue is captured in the audio files and transcripts below:

You can use the worksheet below to capture some of your questions related to learning analytics and data. On the final page, you can export a copy to continue your work.



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Learning Analytics Copyright © by Sheridan College - Centre for Learning and Teaching is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.

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