Dana Center design principles: authenticity
DSC-WAV FacDev 2022
Nicholas J. Horton and Benjamin S. Baumer
June 15, 2022
Dana Center Data Science Design Principle Framework
- The framework is copyright 2021 The Charles A. Dana Center at The University of Texas at Austin.
- The Dana Center has granted educators a nonexclusive license to reproduce and share copies of this publication to advance this work.
Design Principle: authenticity
The course presents data explorations that allow students to address relevant questions that arise in their communities.
Student perspectives
- Recognize specific ways in which mathematics and data are used in everyday decision making.
- Recognize questions that arise in the real world that can be addressed by exploring appropriate data.
- Contribute meaningful questions that can be addressed by exploring appropriate data.
- Identify bias and sources of bias in data, and describe the impact of bias in data on people and society.
- Experience in the process of collecting, cleaning, analyzing, and visualizing data to answer a data- based question of interest.
Faculty perspectives
- Provide opportunities to ask questions of data sets that are relevant to students, both in class and on assessments.
- Provide opportunities for students to ask questions about their school, community, or world that can be addressed by exploring appropriate data.
- Provide opportunities to investigate bias and the source(s) of bias in data and to discuss how bias impacts people and society.
- Provide students with real data, including data that require data processing and cleaning.
Example 1: Open Case Studies