2016 will be the year of personalization in eLearning. As eLearning becomes more and more popular, students will demand more interaction, entertainment, and personalization. Going forward, a one-size-fits-all approach will no longer be the norm. Personalization in eLearning can mean a few different things from creating personalized learning plans and paths based on an individual’s job role, learning style, to capitalizing on students’ personal interests and objectives. Simply, personalized eLearning is student-centered learning in which the learning needs of the individual become the primary focus. Furthermore, it has now been shown that when delivery of blanket content is not interesting enough or specifically relevant that learners become disconnected and uninspired or unmotivated to learn. So, the time has come for the learning department or officers to look at the individual rather than the organization as a whole differentiated and diagnostic within an individual’s personal make up as to what will make the their training program and objectives more effective.
Getting personal with learning content and delivery begins with gaining a better understanding of the learner’s needs, interests, aspirations, and goals. Companies and organization now are taking a deeper dive into data and analytics in order to assess, provide feedback, and determine personalized content and delivery methods. The rise of Big Data and the ability to analyze learning patterns and trends all the way to the individual learner by combing through mountains or terabytes of data is the new way to go as each learner’s “digital trail or footprint” can leave critical clues as to what works, what doesn’t, and how to create specific personal content. A report from the US Department of Education entitled Enhancing Teaching and Learning, Through Educational Data Mining and Learning Analytics suggests:
Educational Data Mining and learning analytics are used to research and build models in several areas that can influence online learning systems. One area is user modeling, which encompasses what a learner knows, what a learner’s behavior and motivation are, what the user experience is like, and how satisfied users are with online learning
. At the simplest level, analytics can detect when a student in an online course is going astray and nudge him or her on to a course correction. At the most complex, they hold promise of detecting boredom from patterns of key clicks and redirecting the student’s attention.