Advanced Adaptivity and Personalization in Learning Systems through Collaborative Recommendations Year Two

Learning Systems are among the most popular e-learning tools in today’s education and training. Most e-Learning systems do not take into account individual aspects of learners (e.g., their goal, experiences, existing knowledge, learning style etc.).The primary goal of the  proposed research is to offer rich adaptivity by combining information from a learner’s profile (e.g. levels, goals, learning style, cognitive abilities etc) with the information from other learners sharing common interests. Based on this combined information, advanced personalized recommendations can be provided, increasing efficiency, performance and learner’s satisfaction. The proposed research will have numerous benefits to the company: (1) Training and learning would become more accessible, to the benefit of employees in small to large -scale enterprises as it will offer unique learning experiences that fully engage and support users (2) It will help in improving and increasing the basic skills of employees, providing the organization with a competitive advantage and hence, will be used to build workforce capability.

Intern: 
Hazra Imran
Superviseur universitaire: 
Dr. Sabine Graf
Project Year: 
2014
Province: 
Alberta
Université: 
Secteur: 
Discipline: 
Programme: