Recommending Learning Objects According to a Teachers’ Contex Model

• Published in: Sustaining TEL: From Innovation to Learning and Practice pp. 470 - 475
• Main Authors: Bozo, Jorge, Alarcón, Rosa, Iribarra, Sebastian
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2010
• Series: Lecture Notes in Computer Science
• Subjects: Annotation Cost; Hybrid Recommender System; Metadata Model; Personalise Recommender System; Recommender System
• ISBN: 3642160190; 9783642160196
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-16020-2_39

Several online repositories make available learning resources known as Learning Objects (LOs), and tasks such as identifying useful metadata, diminishing the annotation effort, and facilitating LOs discovery and retrieval, remain still as open challenges. Advanced searching techniques such as recommending systems have been studied to address these issues, though mainly focused on students. We focus on teachers and exploit their context in order to identify metadata that describes LOs content. Teachers’ profiles consider also such metadata in a hybrid approach for recommending LOs to teachers and instructors.