Canada Spends Logo

Explorez notre analyse du budget 2026

Grant: Collaborative Research and Development Grants

Ryerson University

$149,300.00

Semantics-Driven Learning Adaptation Framework

EF 2013-14

Résumé
The global eLearning industry is predicted to grow to $107.3 billion by 2015, and a large part of its success is due to the adoption of intelligent tutoring and learning systems that adapt and personalize information based on the specific profile, behavior, and intentions of learners. This project will contribute to the growing eLearning sector by providing industrial partners SoJo Ventures and PetaCube with the R&D support they need to build intelligent platforms that deliver this educational content. The outcomes of this project will be implemented as a standalone, open source software platform enabling learners to navigate content in a customized and tailored form.Currently, the systems used by these industrial partners provide only basic adaptation of content based on simple keyword searches or prior content browsing history. However, given their growing client bases, these companies require an intelligent content adaptation framework that is able to understand users needs, their background knowledge, their preferences, and also the specific type of training that they require. This framework would allow the system to select the most appropriate content from its content base and adapt its presentation sequence and style. The project will focus on building an adaptive learning system that semantically annotates educational content and knowledge. Using Semantic Web technologies, the system will make intelligent adaptation decisions for each individual user. The project will accomplish this using the following methodology. First, it will build a content maintenance framework that semantically classifies educational content based on its relevance. Second, it will develop dynamic user models that are semantically interrelated to educational content and prior user knowledge. Third, it will propose and implement content recommendation algorithms that estimate the interest of a user in a subject and provide suggestions on how content should be offered to users. Finally, it will use interaction design principles to ensure that the presentation of content suits users' needs.
Programme
Collaborative Research and Development Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
451209-2013
Name nom
Bagheri, Ebrahim
Department département
Electrical and Computer Engineering
Organization id
43
Institution établissement
Ryerson University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2013
Competition year année de concours
2013
Award amount
149300
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
800
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information and communication services
Area of application fr
Services d'information et de télécommunications
Research subject code
2705
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Software and development
Research subject fr
Logiciels et développement
Partie
1
Nb partie
2
Application title
Semantics-Driven Learning Adaptation Framework
Keyword
NSERC
Application summary
The global eLearning industry is predicted to grow to $107.3 billion by 2015, and a large part of its success is due to the adoption of intelligent tutoring and learning systems that adapt and personalize information based on the specific profile, behavior, and intentions of learners. This project will contribute to the growing eLearning sector by providing industrial partners SoJo Ventures and PetaCube with the R&D support they need to build intelligent platforms that deliver this educational content. The outcomes of this project will be implemented as a standalone, open source software platform enabling learners to navigate content in a customized and tailored form.Currently, the systems used by these industrial partners provide only basic adaptation of content based on simple keyword searches or prior content browsing history. However, given their growing client bases, these companies require an intelligent content adaptation framework that is able to understand users needs, their background knowledge, their preferences, and also the specific type of training that they require. This framework would allow the system to select the most appropriate content from its content base and adapt its presentation sequence and style. The project will focus on building an adaptive learning system that semantically annotates educational content and knowledge. Using Semantic Web technologies, the system will make intelligent adaptation decisions for each individual user. The project will accomplish this using the following methodology. First, it will build a content maintenance framework that semantically classifies educational content based on its relevance. Second, it will develop dynamic user models that are semantically interrelated to educational content and prior user knowledge. Third, it will propose and implement content recommendation algorithms that estimate the interest of a user in a subject and provide suggestions on how content should be offered to users. Finally, it will use interaction design principles to ensure that the presentation of content suits users' needs.