Title :
Learning Object Recommendations Based on Quality and Item Response Theory
Author :
Baldiris, Silvia ; Fabregat, Ramon ; Graf, Sebastian ; Tabares, Valentina ; Duque, Nestor ; Avila, Cecilia
Abstract :
Nowadays, teachers and students continue to face the problem to find high quality learning objects for learning and teaching. The purpose of this paper is to introduce an innovative approach, which considers Item Response Theory (IRT) for recommending to students or teachers Learning Objects (LOs) of high quality in the context of the Learning Objects Economy, which is a marketplace for sharing and reuse of LOs. Recommendations provide to teachers or students the needed support for finding high quality learning objects taking advantage of the previous quality evaluations carry out by peers. An evaluation of our approach was carried out in a real scenario which allowed us to verify the applicability of the process for generating good recommendations.
Keywords :
computer aided instruction; recommender systems; LO; LO reuse; LO sharing; item response theory; learning object recommendations; learning objects economy; learning quality; Context; Educational institutions; Logistics; Mathematical model; Measurement; Standards; Item response theory; learning objects; recommendations;
Conference_Titel :
Advanced Learning Technologies (ICALT), 2014 IEEE 14th International Conference on
Conference_Location :
Athens
DOI :
10.1109/ICALT.2014.238