There’s a lot of buzz surrounding big data. From banks claiming the next big thing for interest rates from your phone company promising you better offers, the huge supersets of data being harvested from personal information, online activity and even buying history is big news for business. But when custom eLearning comes into play, big data can make a big difference in how you interact with learners in the L&D arena. Knowing what your participants are thinking and tracking their learning-based actions helps you build a better machine, so long as you know which information is important and which is just noise.
A Major Case of TMI
When you utilize big data applications and technology to harvest information from your eLearning module, you need to be prepared for a case of TMI: Too Much Information. While it’s helpful to gather metrics based on users and their interactions with the modules, not all news is necessary.
Organizations must sift through and categorize information based on what it helpful to future programs. Some of the need-to-know stuff might include information such as:
• Are users finishing the program?
• Are there users who are failing the post-program quiz several times before getting it right?
• Do you notice that many users are clicking through the course too quickly?
• Are users skipping entire sections?
• Are potential issues broad-based or isolated to just one or two users?
All of these questions can be answered with the right tracking and harvesting of data. Of course, by the same token, there’s plenty of information that you can choose to ignore, especially as it probably won’t make a difference on the design and outcome of your course. The time of day an individual interacts with a program, or the amount of times the module was launched before it was completed won’t matter much unless you notice patterns across all users.
Utilizing Big Data
Once you have the information gleaned from your data sets, you’ll need to decide how to act based on those results. If you notice that many of your users are simply clicking through sections without interacting, for instance, perhaps an option to test out of certain sections would make for happier learners. If the data points to learners who experience the entire course, yet fail the post-course quiz, the content might require some additional tweaking so concepts are clearer.
In essence, big data can help you tell the story of the outcome of your eLearning efforts long before learners act. By interpreting the data and targeting areas which may need fortification, additional design elements or even a complete redesign, you create more effective modules tailored to your learners’ specific needs. Put the buzz to work by tracking learner interaction and then understanding exactly what that interaction means for training and development.