How Data and Learning Analytics Are Driving Change in Education

  • 4 Min Read

Educators are increasingly using data and learning analytics to inform decisions and achieve results.

In the education industry, using learning data and analytics to drive decision-making is a quickly developing practice. While it’s still a very young discipline, administrators and educators all over the world are becoming hungrier for actionable information that will help them to better drive decisions and achieve results.

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Here are six things you should know about learning data and analytics and how they are affecting change in the education industry.

Intentional Disruption of Education

Many have said that traditional education is being disrupted by new advances in educational technology and I believe this to be true, especially when looking back to the now antiquated lecture halls from my own college experience.

I think one of the true benefits of that disruption is the increasing ability to provide an optimum, personalized learning experience that meets the individualized needs of every learner. Learning analytics are a critical element in being able to deliver that kind of experience. They provide the basis to know and understand the behavior, needs and experiences of the learner, and, more and more, they’re being used to do so.

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Prioritized Learning Outcomes

One of the big challenges when it comes to learning analytics and data is the sheer volume of data that’s available. Modern LMS platforms make it easy to collect vast amounts of data within the learning environment.

Identifying what is important and prioritizing which elements to analyze first is where the true strategy comes into the picture. Organizations should prioritize learning data and analytics collection based on identified learning outcomes as well as their respective values, mission and vision.

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What Data to Collect and How to Use It

With so much data out there, only fractions of learning data collected are usually used. That data will include a variety of data points, like engagement data, time on task, activity performance and achievement. It should all be taken in the context of the learner and their specific circumstances and needs. Any special assistance, disabilities, cultural differences or other influencing factors must always be considered.

Data must always be interpreted within the right context. On its own it doesn’t mean much and can easily be misinterpreted.  It’s when we provide context, apply objectives and strategies and begin to evaluate those elements that data becomes valuable, is transformed into knowledge and information, and actions can be taken to achieve results.

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Data Security

Knowing which data will be analyzed or used in decision making processes is one of the foundational steps in determining what data to collect. A solid data governance and security policy is to only collect data that’s useful and relevant. By limiting the data, you collect and store relevant data elements, which decreases your risk of data exposure.

The institution is ultimately responsible for protecting the privacy and security of the data it collects.  However, learners should have the right to express their opinions about the usage and exposure of collected data that’s about them.

Policy Changes

Government changes often impact how we collect data, align it to various standards and outcomes, and fulfill reporting requirements. With the new federal administration coming into office in the United States, I do expect that some requirements will change.

You should take into consideration the impact of both federal policy changes and your own policy changes on the data that you collect and what you report. This may impact how you want to measure learning.  These policy changes could require you to change what you did previously, and could impact the data being tracked through your learning environments and technologies.

A final note about Personalized Learning

While personalized learning is one of the strongest benefits of technologies driven by learning data and analytics, institutions should understand the differences between personalized and prescriptive learning, and ensure that both teachers and learners are exposed to a wide variety of learning methods and content.

I believe this is true more so for the elementary and secondary market segments. As those learners go through roughly 12 years of learning, it’s very likely that the way in which they best absorb learning may change, and the technologies and data should enable us to deduce those changes. Otherwise we may end up creating very prescriptive and narrow paths for students.

 

 

 

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