Adaptive learning technologies (ALT) that customize content and instruction to individual students have long-held promise for enhancing learner motivation, engagement, and outcomes. However, comprehensive learner modeling within adaptive learning technologies is crucial when identifying the appropriate systems to know what adaptations will benefit particular students. A key challenge currently facing the field of adaptive learning technologies is utilizing comprehensive learner profiles that can be effectively matched to personalized interventions within the ALT.
While current systems aim to collect data about the user to tailor learning, three primary issues arise: developers must (1) select appropriate tools and methods to gather meaningful learner data continually, (2) map this profile data to suitable adaptive interventions, and (3) understand how users experience the ALT, all the while maintaining learner engagement and motivation while interacting with the ALT. This presentation synthesizes findings from recent studies that employ learner profiles within adaptive environments in formal education settings (K-12 and higher education), offering developers guidance to optimize learner information use.
By highlighting the integral role of motivation and proposing strategies for inspiring active learner participation, this work empowers developers to create adaptive systems that floor rather than bore students. Session attendees can expect a discussion of key concepts and case studies grounded in practice.
Brian Bays
Robin Dazzeo