Details Make A Difference: How EDUCAUSE Used Data Science Methods to Create Peer Groups with Purpose

Analytics8 developed an advanced analytics solution that allowed EDUCAUSE to segment its member institutions based on various datapoints and make it available to its member base. We did this by:

• Analyzing an existing dataset from EDUCAUSE to compare institutions based on public information such as degree offerings, size, public/private, and sports affiliations and then adding additional features using Python to enhance the dataset.
• Performing feature selection to determine which of the 80 features were the most informative or influential to creating potential peer groups.
• Creating a machine learning model to select the 15 features that contained the most information and would best lead to the identification of peer groups.
• Creating an analytics solution using Qlik Sense that integrated the machine learning model and showed the results of each cluster and what comprises each group. The solution includes peer group scores, comparison analysis, and ability to drill down into individual institutions.

The analytics solution will provide the association the ability to easily recommend peer groups to members based on data science and further promote their own engagement with the peer-grouping tool. This new process will allow the association to better serve its members and continuously learn the different ways the academic institutions can influence each other.

The robust proof-of-concept has set up the association to retrain the machine learning models as it continues to collect more data from members—something EDUCAUSE is actively doing. With this solution, EDUCAUSE will be able to:

• Provide guidance on what makes a good peer, which institutions each member should be comparing against, and why based on a scoring system.
• Provide data on institutions based on an IT perspective—project level, budget level, and resource level—and identify which are similar or which ones are different but can still provide value because of aspirational goals that have been identified.
• Fill in the gaps where there is a lack of research participation within peer groups and recommend alternative peers based on data science.

The machine learning data model offers value to those members that will still only want to use the basic peer groups because they will be able to see the evaluative score of how similar EDUCAUSE thinks their peer group is to their institution. And for those members that want the custom-level peer grouping, they will find value in the recommendations made available to them.