4 ways learning analytics lead to equity in higher ed

80% of higher ed survey respondents say they or their institution uses student data
By: | September 9, 2020
To build equity through learning analytics, college leaders must set explicit goals while ensuring transparency and privacy. (GettyImages/Igor Alecsander)To build equity through learning analytics, college leaders must set explicit goals while ensuring transparency and privacy. (GettyImages/Igor Alecsander)

Learning analytics can guide higher education leaders in building equity and expanding access as colleges and universities face the COVID pandemic and recession amidst renewed protests over racial injustice.

The Learning Analytics and Equity Toolkit, developed higher ed consultants Tyton Partners, covers four guiding principles for using learning analytics to make policy, practice, and pedagogy more equitable for underrepresented students.

In a survey, done in conjunction with the Every Learner Everywhere Network, 80% of higher ed respondents said they or their institution used student data.

But only 40% reported that their institution was leveraging student data to address performance gaps, and 75% said their school had not set clear goals for use of analytics.


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And over 60% of the respondents agreed that their institution had a common definition of equity but only 40% said their school had a strategy to address equity gaps.

How learning analytics lead to equity

Over 70% of the survey’s respondents were confident learning analytics can create stronger and more equitable instruction.

Here are the report’ four guiding principles for using learning analytics to build equity:

1. Set explicit goals for achieving equity in academic outcomes across students of all backgrounds: Leaders must define course-level learning outcomes across and create cross-discipline communities to interpret data and share best practices.

2. Ensure inclusion and support for faculty, administrators and students: Campus leaders must include all stakeholders when establishing policies that ensure the privacy and ethical use of data.

3. Establish policies for data transparency: Lack of awareness around security, data access and student privacy can limit the effectiveness of learning analytics. Leaders must provide professional development to guide faculty in integrating analytics platforms and report progress regularly.

4. Ensure that technology and infrastructure are easy to use: Leaders should create a centralized source of student learning and demographic data, and ensure all stakeholders have appropriate access. Leaders should also develop a long-term vision for meeting learning analytics goals.


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