Personalized learning provides a unique, highly focused learning path for each student. Individual attention from instructors isn’t feasible in traditional educational models with large numbers of students, and personalized learning is intended to use IT systems and tools to tailor learning experiences based on student strengths, weaknesses, and pace of learning. Technologies including analytics, adaptive learning, digital courseware, and others underlie personalized learning, which builds a “profile” of each student and makes continual adjustments to learning paths based on student performance.
Adaptive learning is one technique for providing personalized learning, which aims to provide efficient, effective, and customized learning paths to engage each student. Adaptive learning systems use a data-driven—and, in some cases, nonlinear—approach to instruction and remediation. They dynamically adjust to student interactions and performance levels, delivering the types of content in an appropriate sequence that individual learners need at specific points in time to make progress. These systems employ algorithms, assessments, student feedback, instructor adjustments/interventions, and various media to deliver new learning material to students who have achieved mastery and remediation to those who have not.