BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile

Learning Objects (LOs) are distinguished from traditional educational resources for their easy and quickly availability through Web-based repositories, from which they are accessed through their metadata. In addition, having a user profile allows an educational recommender system to help the learner to find the most relevant LOs based on their needs and preferences. The aim of this paper is to propose an agent-based model so-called BROA to recommend relevant LOs recovered from Repository Federations as well as LOs adapted to learner profile. The model proposed uses both role and service models of GAIA methodology, and the analysis models of the MAS-CommonKADS methodology. A prototype was built based on this model and validated to obtain some assessing results that are finally presented

Dataset-driven research for improving recommender systems for learning

Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., & Duval, E. (2011). Dataset-driven research for improving recommender systems for learning. In Ph. Long, & G. Siemens (Eds.), Proceedings of 1st International Conference Learning Analytics & Knowledge (pp. 44-53). February, 27-March, 1, 2011, Banff, Alberta, Canada. http://dl.acm.org/citation.cfm?id=2090122&CFID=77368864&CFTOKEN=72282583In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or EachMovie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for Technology Enhanced Learning (TEL). We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to augment explicit relevance evidence in order to improve the performance of recommendation algorithms.dataTEL, STELLAR, AlterEgo, VOA3

Validación de un sistema inteligente de recomendación híbrida en federaciones de repositorios de Objetos de Aprendizaje

En la actualidad los objetos de aprendizaje OA, que son recursos educativos cuyacaracterística distintiva son los metadatos descriptivos, han tomado gran importancia para apoyar los procesos de enseñanza aprendizaje de estudiantes que cada día tienen acceso a más información; la dificultad al recuperar los OA se encuentra al especificar las palabras o cadena de consulta, para hacer las búsquedas y encontrar un recurso que se adapte a lo que se quiere encontrar y a sus características específicas. Es allí donde surgen los sistemas de recomendación, como apoyo a los usuarios a encontrar OA relevantes, en este artículo se realizan pruebas de funcionamiento de un sistema de recomendación híbrido aplicado a diferentes repositorios de OA y federaciones de repositorios, con el fin de validar su uso y pertinencia al momento de acceder a un recurso educativo

Usage-based Object Similarity

Recommender systems are widely used online to support users in finding relevant information. They can be based on different techniques such as content-based and collaborative filtering. In this paper, we introduce a new way of similarity calculation for item-based collaborative filtering. Thereby we focus on the usage of an object and not on the object's users as we claim the hypothesis that similarity of usage indicates content similarity. To prove this hypothesis we use learning objects accessible through the MACE portal where students can query several architectural repositories. For these objects, we generate object profiles based on their usage monitored within MACE. We further propose several recommendation techniques to apply this usagebased similarity calculation in real systems