Using Big Data To Improve Online Learning: Why Successful eLearning Modules Need Big Data
The widespread adoption of digital technology has created an explosion of data. In fact, every time you use digital technology, you’re leaving a digital footprint of your activity. And in recent years, it has become possible to collect, aggregate, analyze, categorize, and learn from all of this data. This was the dawn of Big Data [1], and it made possible the ability to learn from the behaviors of people using digital technology.
Companies collect and analyze big data for a wide range of purposes—everything from feeding the hungry to preventing crime to optimizing marketing campaigns. Most of the time, companies collect big data from external sources to help the business serve customers more effectively, become more efficient, and increase profits.
Of course, data isn’t meaningful unless you can analyze it. This is done by creating models based on the data, then running tests to observe the results. Analysts look for patterns and insights to help solve a problem. Tweaks are made and more tests are run, until the results match the goals.
Using Big Data Τo Analyze Learning
Before the rise of Big Data, instructors had to rely on periodic tests and assessments to judge the progress of their learners. Often, struggling individuals were identified too late, and the work to catch up would often require enormous effort.
But as Big Data began to spread into every type of industry, chief learning officers realized the opportunities for improving learning experiences. If we could understand learners’ behaviors and activities, and find correlations to their learning success or failure, we could help people become more successful in their online courses.
This use of Big Data to improve online learning is called learning analytics. This is how learning analytics works:
Learning Management Systems like Moodle collect a vast amount of user data. Every time a user interacts with a learning module, dashboard, forum, assessment, or communication tool, the LMS records and stores that information.
This data can be sorted, filtered, and correlated to specific metrics, such as activity and learning success.
As patterns emerge, instructors and course designers can make adjustments to the course that will help struggling learners to become more successful. For example, there may be a specific set of behaviors that acts as an early warning sign that a student will fail. If a learner exhibits these behaviors, the instructor can be alerted to reach out to the student and intervene.
Research shows that learners recall more when they’re more engaged with the course material. Learning analytics (LA) makes that possible by tracking users’ activity to understand where they are most and least engaged with the module. It then becomes possible to create personalized eLearning courses that break out of the one-size-fits-all paradigm.
This use of Big Data in an eLearning environment creates a feedback system that can help instructors and course designers to discover solutions to the most common problems in online learning.
The Future Οf Learning Analytics
As digital technology advances, so will the possibilities for using LA in new ways. For example, although LMS data provides powerful insights, it doesn’t give a full picture. Data is siloed across systems, even within organizations. But there’s a movement toward greater interoperability, which will break down the silos and provide a more detailed understanding of learners’ needs.
Adaptive learning [2] is another development on the horizon. Adaptive learning leverages artificial intelligence to adapt a learning module to a specific user in real time, based on the learner’s activities and performance. Data from learning analytics can be applied immediately to optimize each person’s eLearning experience.
As the rise of Big Data has provided opportunities to understand customers and increase sales, it has also created tremendous opportunities to make companies more profitable internally. Organizations that use learning analytics to improve the their online training modules can also see greater efficiency and profitability as well.
Go deeper with learning analytics—check out our on-demand webinar, 5 Tips for Data-Driven Learning.
Footnotes:
- The Complete Beginner's Guide To Big Data In 2017
- The Evolution of eLearning and Learning Analytics
References:
- Free eBook – Big Data For HR: How Predictive Analytics Can Deliver Business Value
- 4 Ways Predictive Learning Analytics Decreases Ineffective Learning
- 7 Steps To Successfully Implement Learning Analytics In Your Company
- What’s Missing In Your Online Training Evaluation - And How To Fix It
- 4 Tips For Improving Online Course Design With Learning Analytics
