摘要：Normal 0 21 false false false ES-HN X-NONE X-NONE MicrosoftInternetExplorer4 In modern e-learning, Learning Objects Management Systems (LOMS) are a new key piece for the interchange of resources. LOMS are repositories specialized in search and recovery of learning objects (LO). LO is a particular instructional resource that can be used as basic unit of information and interchange between e-learning applications. However, quality in LO is a problematic present in many of LOMS management activities. In this work a Quality Evaluation Model for Learning Objects (MECOA, from a Spanish acronym that means it) is presented. MECOA proposes a model to evaluate the learning objects from a pedagogical perspective, using indicators collections grouped in six categories and it defines a linguistic label set for each one of indicators. An instrument for MECOA was implemented into an own LOMS called AGORA (from a Spanish acronym that means Help for the Management of Reusable Learning Objects). The obtained data is source of important pedagogical information, especially in the learning object management process; due that, this quality evaluation information is added to the metadata of the object and could be retrieved along with the OA within LOMS. As example of this, a rule set was obtained by means a knowledge extraction methodology. The generated rules are IF-THEN type and can be used for improvement some learning object related task like search, tagging and sequencing from recommending perspective.
关键词：Keywords - Learning Objects, Data Mining, knowledge extraction, Quality
