Abstract
Personalized adaptive multimedia environments provide individual learners or learner groups with experience that is specifically tailored to them. To achieve effective personalization, a variety of information about the learner is required. Tailoring multimedia environments to individual learner cognitive characteristics is becoming a major means for achieving a true learner-centered experience for learners through their interaction with multiple content sources, presentation formats, and delivery means. Personalized multimedia environments are capable of realizing advanced learning and instruction strategies based on a continuous process of adaptation between the learners and instructional systems. This adaptation process could be accomplished through personalized interaction and adaptive presentation of content, learner feedback, adaptive navigation and search, and different adaptation methodologies. As was mentioned in earlier chapters of this book, a major instructional implication of the expertise reversal effect is the need to tailor dynamically instructional techniques and procedures, levels of instructional guidance to current levels of learner task-specific expertise. In online multimedia instructional systems, the levels of learner task-specific expertise change as students develop more experience in a specific task domain. Therefore, the tailoring process needs to be dynamic, i.e. consider learner levels of expertise in real time as they gradually change during the learning sessions. This Chapter describes general approaches to the design of adaptive learning environments from the perspective of tailoring learning procedures and techniques to individual cognitive characteristics of learners. Studies in aptitude-treatment interactions offered a possible approach to adaptive instruction. Intelligent tutoring systems and adaptive web-based hypermedia systems use learner models to tailor learning tasks and instructional content to individual learner characteristics. This approach accommodates learner characteristics (e.g., knowledge, interests, goals) into explicit learner models that guide adaptive procedures. On the other hand, advisement and adaptive guidance approaches realize a greater learner control over instruction and provide individualized prescriptive information in the form of recommended material and tasks based on learner past performance.