The software that visualizes data trends and helps organizations make informed, key decisions has significantly evolved in recent years. Higher education institutions are finding innovative ways to leverage this data in order to measure how engaged their students are, and to identify ways to help them reach success; however, there is still more they can do, and in different ways, to better measure student engagement.

The Current Climate for Measuring Engagement

As higher education progressively uses more technology to support teaching and learning, institutions have access to data from software like learning management systems (LMS) that indicates how users interact and perform throughout their learning path. Access to this data lets institutions explore new ways of understanding how faculty are teaching and how students are learning.

Pairing edtech data with other variables like demographics or historical performance is often key to providing efficient, personalized support for student engagement and success. While some institutions work with vendors to predict student engagement and success, others are designing their own models and systems that help them identify warning signs that a student will drop out or graduate late based on the unique conditions and systems in use by their campus community.

Yet, access to, and optimization of, data from educational technology is maturing. Institutions need more than just access to raw data or databases; they need optimized data, in a consistent, standardized format, and delivered fast.

In some cases, the data must be delivered in real-time, so institutions can analyze it on-the-fly in order to reach students—or their advisors and coaches—in the moment, and with the context in which, they might need support.

(Next page: The future of using data for engagement, and how to get there)

About the Author:

Jared Stein is the VP of Product Strategy & Research for Canvas by Instructure.

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