Here’s our take on this new trend — and some compelling reasons to jump on board with big data in eLearning.
What is big data?
The phrase “big data” is a bit of a misnomer. It suggests a huge quantity of information — maybe you’re picturing mountains of paperwork piled on a desk — but big data is less about the volume of information than its complexity. For example, perhaps you are only tracking 25 learners, but their information is spread out in different platforms — mobile, social, LMS. That’s still big data.
Big data can also pull in other relevant information from peripheral sources. For example, a pizza delivery service may collect weather and power outage data for particular areas, and then target mobile coupons to those who are less likely to want to go to a restaurant or cook dinner.
How does big data work in eLearning?
The big data produced by online learners is dependent on the ways that the course collects information. Most LMS systems track basic data like course progress, activity completion, and assessment results.
Many e-authoring tools fine-tune the collection as well. For example, Articulate Storyline modules can produce SCORM packages that deliver precise information to your LMS — how many screens a user clicked, whether they answered questions, what they typed, and how long they spent on each screen.
How can big data improve your online course?
As you can see, being able to collect and manage complex sets of data on learners opens up new possibilities for creating more effective eLearning. Here are four ways that big data can help improve your online courses:
1. Gather comprehensive feedback about the learner experience.
In the past, monitoring the learner experience was difficult in online environments. Instructors delivered a product that learners engaged with privately, which mean that the benefits of in-person instruction — reading body language cues for boredom, hearing live comments from learners — were removed.
But with big data, you can gather all kinds of information about your learners, including their interests and demographics, the types of technology they prefer, and even how long their attention spans are. Comparing this information to their performance in the course can help you hone in on who your audience is and what they need to learn most effectively.
2. Create a more cost-effective development process.
The course-development process is slowed by design decisions that sometimes have to be made in the dark: what type of activities will our learners respond to? What kinds of content keep them engaged? How long can we hold their attention on this particular topic?
Big data sets can begin to formulate reliable answers to these questions ahead of time, cutting down on guesswork and saving designers a chunk of time (and companies money).
3. Develop personalized lessons that speak to each learner.
There’s a growing body of research that shows the benefits of personalized and adaptive eLearning: learners recall more content when they are challenged appropriately, and they are more engaged when the learning is tailored for their answers and preferences. Big data can form a picture of particular learners and better enable developers to create learning scenarios that speak to them.
4. Assess learner progress.
This one is the most simple, but it’s the single most important piece of data in the eLearning process. Without being able to track and monitor learner progress, eLearning experts have no way of assessing success of the course, following up with learners who are struggling, or beginning to generate the patterns that help clarify who is learning, and why.
In the end, big data turns out to be a big deal. The complexity of eLearning data has given rise to the big data trend, while the success found by companies who have implemented this strategy will continue to buoy the big data trend for years to come.