Let’s begin with an assumption. You have a good learning strategy in place that is a judicious mix of instructor-led and online courses to take care of the training and development needs of your employees. But, the question that you need to ask yourself is, even if I have online courses that are good for all my employees doing a particular job, how do I find out or measure when during an elearning course do learners go astray?
The challenge before academicians or educators/instructors of any kind is to sustain learners’ interests throughout the courses intended for them. However, with technological advancements bursting at the seams, this challenge can be converted into an opportunity. How? Through analytics.
If you are a regular online shopper or frequently use websites to make your travel bookings, you will be aware of how these websites provide you with information, deals, products/ services that get as much personalized as you browse them. This is being done by using big data that makes it possible to analyze data which so far was incomprehensible, incoherent and useless; and convert it into meaningful information that defines user behavior and preferences. The next time the user visits that website, it remembers his/her browsing history and click behavior and accordingly adjusts the shopping or booking experience.
Just like businesses use business analytics and business intelligence to do more with less; similarly, Learning Analytics is making it possible for educators to tailor learning content to the needs and preferences of their learners so that the experience is more personalized and therefore, engaging. Though it is an area still being explored, Learning Analytics makes it possible to track learner engagement as well as the global development of new concepts (particularly those involving hard-core R&D) based on individual or enterprise social learning activities.
Educational Data Mining is another offshoot of Learning Analytics that combines computational and psychological methods to determine the way learners learn and develop interest. Elearning systems, mobile learning formats, serious games and simulations too can be mined for educational data. Inferences from this data are then used to segment or profile learners into groups so that similar learning content and experiences can be created for learners belonging to a particular group.
Just like big data empowers organizations to have competitive edge over others in the market by letting them know the exact consumer behavior, analytics can make learning a lot better by treating seemingly useless data and converting it into meaningful information that will save the resources of the organization and reduce the chances of employees’ disengagement with learning.
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