Datification and Data Justice

Concurrent Session 9
Equity and Inclusion

Brief Abstract

This emerging ideas session looks at a work in progress with collaboration opportunities for educators who would like to look at the data collected on students from an open and social justice perspective. This includes how students are informed, predictions and preemptions, algorithmic governance, and social-justice-informed design.


Chris Luchs is an Associate Dean for Career and Technical Education in the Colorado Community College System. He has over twelve years of experience teaching online and f2f at the community college level. His current happy places are analytics, data visualization tools, and reading LitRPG. Chris is also currently pursuing a PhD in Community College Leadership at Old Dominion University.

Extended Abstract

With most of Higher Education focusing on leveraging big data, data analytics, and data governance, one area that is overlooked is what rights do our students have when it comes to how their data is collected and used?  This emerging area has been named data justice and focuses on discussing how learned data is collected, how might this skew learner data, and what options should we be offering our students? Data justices also provides a unique framework to evaluate both the positive and negatives aspects of higher education’s increasingly data driven environment.

The emerging idea of data justice focuses on the intersection of big data, data analytics, and social justice. It offers a new way to look at our processes, procedures, and policies when it comes to student data and how we use it. Data justice is unique as it allows educators to evaluate both the positive and negative aspects of data acquisition and use.

In this session, presenters will create a short ten minute electronic presentation to elicit responses from peers on their project looking at how colleges and universities are collecting and using student data to improve student success and achieve their missions and strategic goals.