How Learning Data and Analytics Are Driving Change in Education

In the education industry, using learning data and analytics to drive decision-making is a rapidly 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 make more informed decisions and achieve results.

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

1. Intentional Disruption of Education

Many have said that traditional education is being disrupted by new advances in educational technology. This appears to be true, especially when looking back at the now antiquated lecture halls of the past. One of the true benefits of educational disruption is the ability to provide optimal, personalized learning experiences that meet the individual needs of every learner.

Student data is a critical element in being able to deliver those kinds of experiences. It provides the basis for your organization to understand each learner’s needs and preferences, and—more than ever—you can use the learning analytics derived from learner data to create more engaging learning pathways.

2. Prioritized Learning Outcomes

One of the biggest challenges when it comes to analyzing learner data is the sheer volume of information that’s available. Fortunately, modern learning management systems make it easy to collect vast amounts of data directly within the learning environment.

Identifying the data that’s most important to you and prioritizing which elements to analyze first is where the need for a well defined data strategy comes into the picture. Your organization should prioritize the collection of learning data and analytics based on your identified learning outcomes as well as your unique values, mission, and vision.

3. Data Collection

With so much data out there, only a fraction of the learning data that organizations collect typically gets used. That data will include a variety of data points, like adoption, engagement, time on task, activity performance and achievement. All of this information should align to each learner and their specific circumstances and needs. In particular, organizations must always consider special assistance, disabilities, cultural differences and other factors that influence individual learning needs.

It’s critical that your organization analyzes and interprets its data within the right context. On its own, data doesn’t mean much and your stakeholders can easily misinterpret it.  It’s when you provide context, apply your objectives and strategies, and begin to evaluate progress that your data becomes valuable and can help transform your knowledge into action that achieves results.

4. Data Security

Establishing a plan for how you’ll analyze information and use it 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 to your strategic goals. By limiting the data you collect and storing only relevant data elements, you’ll decrease your risk of data exposure.

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

5. 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.

6. Personalized Learning

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

This is true more so for the elementary and secondary market segments. As those learners go through roughly 12 years of learning, it’s likely that the way they best absorb knowledge will change. Learning data and analytics should enable you to deduce those changes. Otherwise you may end up creating paths that are too prescriptive and narrow for learners.

Want to learn more? Download the Ultimate Guide to Learning Data and Analytics to discover how you can unlock the power of your data to become a world-class learning organization.

Written by

Michael Moore

Michael Moore is a Senior Advisory Consultant (Analytics) with Desire2Learn. Having worked for over twenty years in the software industry, Michael’s created numerous customization solutions and focuses on analytics, assessment reporting, and competency-based education models. Previously a Desire2Learn higher education client, he managed the Desire2Learn Analytics implementation at Daytona State College for three years. Michael holds a master’s degree in Computer Information Systems and a bachelor’s degree in Accounting.