PERSONALISATION OF E-LEARNING CONTENT VIA METADATA

摘要

The increasing importance of eLearning and its role inrnmodernizing the teaching and learning methods can bernseen from the huge amount of new offered eLearningrncourses and research projects. Although, new technologiesrnand in particular the Internet has opened many newrnopportunities that are of a big interest for eLearningrnenvironments, these opportunities have not yet beenrnexhausted. The current eLearning environments offer littlernor no support for adaptivity. Therefore, developing newrnadaptive eLearning environments that are able to satisfyrnthe user’s requirements is of a big interest nowadays. Thisrnpaper addresses the adaptivity issue and describes thernarchitecture of a new Metadata-driven AdaptiverneLearning Environment (MAeLE), which is a newrnframework for personalized adaptive eLearning. The basicrnprinciple of MAeLE depends on delivering adequaternmetadata for the user as well as for eLearning content.rnThe eLearning content itself does not contain anyrnsequence logic or metadata. User metadata are updatedrnfrom time to time depending on the user’s behavior in thernoffered courses. The metadata correlate to the contentrnmetadata that mainly are defined in relation to thernLearning Object Metadata (LOM) standard [16].rnAccording to the user characteristics existing in the userrnmodel, the eLearning content, eLearning strategy and arnnavigation method are selected and updated from time torntime correspondingly. A main advantage of MAeLE is itsrnflexibility, extensibility and compatibility to the SharablernContent Object Reference Model (SCORM) [1], [18].