Unlike machine learning, predictive analytics still relies on human experts to work out and test the associations between cause and outcome.
Joining akin learning streams across your organization would create an even richer set of potential insights. For instance, thus, there is a need for solutions to foster interaction and communication between MOOC participants by bringing together face-to-face interactions and online learning activities.
The primary implication for learning analytics practitioners is the need to interpret quantitative analysis procedures at every phase from philosophy to conclusions. And also, the increase in and usage of sensitive and personal employee data present unique privacy concerns. Equally important, comparing online and face-to-face learning experiences for non-traditional employees.
By the same token, employees motivation is an important factor in the success of online learning, it does it quickly and cheaply with links to external content, making the learner type in concepts, numbers or full sentences, which are interpreted by AI. As a matter of fact, learning analytics has paved the way for learning dashboards to appear in order to provide a visual interpretation on the progress of employees.
Want to check how your Learning Analytics Processes are performing? You don’t know what you don’t know. Find out with our Learning Analytics Self Assessment Toolkit: