Framework for Metacognitive Mapping to Design Metadata for Intelligent Hypermedia Presentations

This article presents a conceptual model and content framework to aid in the linkage of cognitive variables, to the metacognitive attributes of those variables, and related metadata for the design of the presentation media. Instructional designers seeking to provide mediated instruction to target specific learning strategies, in specific target markets, or for specific individuals, may use the framework to help align the design elements comprising the instructional presentation, with the metacognitive learning styles of the target population, with the cognitive variables governing the subject matter presentation. The framework was designed to aid in the addition of automated intelligence to presentations and the variables, attributes and elements were selected and organized for this purpose. The research foundation integrates traditional cognitive science and learning theory with current thinking in these areas from the artificial intelligence perspective. Instructional design professionals have long excelled in the application of proven media design principles to the education or training task at hand. The basic media design principles, and their underlying instructional strategies, have evolved over time and through many generations of media. Hypermedia and client/server communications have been the latest in a long series of technical innovations, and researchers have successfully developed design principles to implement advanced interactivity and networking into their mediated instruction. The latest of the technical innovations applies an area of artificial intelligence, which deals with the gathering of user information. This information may range from demographics, to test scores, to user-specific options. The commercial interests, which have pioneered this technology, are using it to develop personalized online shopping experiences, and to improve Internet-based direct marketing campaigns. Instructional designers can use this same technology to personalize and customize learning experiences--including the content presented and the interactions with that content. This would be only the initial stage, and the long term potential of software programs able to intelligently interact with users, and to simultaneously perform content customization with large numbers of users opens new worlds for the next generation of multimedia and hypermedia design. Instructional design frameworks are needed to help guide the development of the projects. This will include the integration of the theoretical, conceptual, and practical aspects of the subject matter. The next generation of technical innovation will likely add a layer of intelligence to media design, and open possibilities for mapping this intelligence to the metacognitive attributes of users. A framework is needed to guide the theoretical and practical application of this intelligence and to help align it with the learning objectives and learning styles specific to a user or target population. Such a framework is the subject of this paper. FRAMEWORK BACKGROUND AND THEORETICAL BASIS This article develops a framework to help hypermedia designers address content personalization and customization. This is accomplished through structures which align the metadata which represents the media design variables with the metacognitive attributes, which determine the receptivity of the learner to specific learning situations and environments. The framework begins with the underlying cognitive variables, which determine the basic category of the learning and intellectual processes to which the media and instruction will be addressed. A higher level of abstraction leads to metacognitive attributes, which address strategies for knowledge acquisition, understanding, and learning. Metadata design elements are drawn from these metacognitive attributes. The framework provides a basis for the addition of intelligence through the linkage of user-specified options to underlying design elements, and subsequently to learning strategies. …