The New Media Consortium recently published a report on the technology trends and developments they foresee in the next five years, in higher education institutions. Adaptive Learning and Learning Analytics were identified as two important developments emerging in the educational technology market for higher education. Learning Analytics has become a well-established term in the eLearning landscape, as reporting and analytics tools have become high in demand. Adaptive learning is a newer development, nurtured by the data provided with learning analytics.
"Analytic data empowers educators with information that allows them to shape better learning pedagogies."
Learning analytics is data pulled from reports and dashboards (through an LMS reporting and analytics tool), that helps develop learner profiling. Learning analytics first emerged with a focus in hindsight, preoccupied with describing results and providing a diagnosis. Over the years it has transformed to its current state, which is predicting what will happen in the future. This is evident
As an example, The University of Tennessee at Chattanooga is currently using learning analytics to identify which areas of learning and the curriculum students are having the most trouble with. What they found was that students were switching majors based on a difficult English class they were trying to avoid. This type of insight and information is extremely advantageous for institutions because it highlights which weak spots exist for the school, and for students, so they can take steps to remedy these in the most efficient way possible.
Adaptive learning is a computer/technology based method of learning, that uses learning technologies that modify the presentation of learning materials to adapt to individual learner needs, often influenced by the data gathered from learning analytics. Modern teaching tools can now essentially learn the way people learn, and adapt learning
Adaptive learning will assist higher ed institutes who are ramping up their efforts to pinpoint the students who are likely to drop