Continuing their mission to advance higher education through the use of information technology, Educause has published “Adaptive Learning Systems: Surviving the Storm,” written by Lou Pugliese, Senior Innovation Fellow and Managing Director at the Arizona State University (ASU) Action Lab. This essay explores, in extensive detail, the need for educational institutions, vendors of edtech, and other stakeholders to establish baselines to create a shared foundation for the continuing development of adaptive learning systems:
Intelligent adaptive learning systems are quickly emerging but are still in experimental stages. The intended design of these data-adaptive solutions seeks to enable differentiated instruction at a personalized level of learning. New approaches to diagnostic and formative assessment design making use of adaptive intelligence are becoming more common. Adaptive learning systems are designed to dynamically adjust to the level or type of course content based on an individual student’s abilities or skill attainment, in ways that accelerate a learner’s performance with both automated and instructor interventions. These adaptive systems achieve this by helping to address learning challenges such as varying student learning ability, diverse student backgrounds, and resource limitations. The intent of these machine learning systems is to use proficiency and determine what a student really knows and to accurately and logically move students through a sequential learning path to prescribed learning outcomes and skill mastery. These specific features will transform first-generation digital learning systems.