Fab: content-based, collaborative recommendation
Communications of the ACM
Knowledge engineering: principles and methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Web-based education for all: a tool for development adaptive courseware
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE
User Modeling and User-Adapted Interaction
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Design and evolution of an undergraduate course on web application development
Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education
Personalization in distributed e-learning environments
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
Collecting community wisdom: integrating social search & social navigation
Proceedings of the 12th international conference on Intelligent user interfaces
Technology supports for distributed and collaborative learning over the internet
ACM Transactions on Internet Technology (TOIT)
eTeacher: Providing personalized assistance to e-learning students
Computers & Education
A pedagogical interface for authoring adaptive e-learning courses
Proceedings of the second ACM international workshop on Multimedia technologies for distance leaning
Groupized learning path discovery based on member profile
ICWL'10 Proceedings of the 2010 international conference on New horizons in web-based learning
| Hi-index | 0.00 |
Existing methods support adaptive e-learning mainly by setting student characteristics in a student profile, and use it as a filter to extract suitable learning content from a dedicated structure of course materials. If simple student characteristics, such as prior knowledge and learning preference, are considered, it may be straightforward for an instructor to set up the student profiles. However, if complicated student characteristics, such as learning styles, interaction styles and content styles, and other factors that affect the students' interests on the course materials are involved, it may become too difficult for an instructor to design a suitable course structure matching all these criteria. It is also complicated for system implementation as many rules need to be set up. In this paper, we propose a three-tier profiling framework in conjunction with a concept space structure and a set of concept filters to address the above problems. The framework offers a unified way to model and handle a variety of student learning needs and the different factors that affect course material relevance. The framework is extensible in nature and can form the foundation for the future development of adaptive e-learning systems.