Learner Modeling in Continuously Adapting Hypermedia

Adaptive Hypermedia offers a technological solution for individualized and optimized online learning. However, effectively adapting web-based educational systems to individual learning traits remains an open issue. This work investigates adaptivity based on an innovative learning style cycle approach, which allows for the observation of learner interaction behavior over instructional events designed to attend to all different learning styles. The ADaPtor system was developed with the goal of optimizing learning efficiency and effectiveness by gradually and iteratively adapting the learning cycle based on learner behavior and performance. ADaPtor's adaptivity scheme personalizes presentation, content and navigation to satisfy individual learning needs. Three learning cycles were developed for evaluating ADaPtor. Results were encouraging: learning efficiency was optimized while effectiveness was comparable to a control group when learners started following adaptive recommendations more often. This research provides the online learning community with a tool for using a learning style cycle in adapting to individual learning traits.