In the last years, the development of different Repositories of Learning Objects has been increased. Users can retrieve these resources for reuse and personalization through searches in web repositories. The importance of high quality metadata is key for a successful retrieval. Learning Objects are described with metadata usually in the standard IEEE LOM. We have designed and implemented a Learning Object Metadata ontology (LOM ontology) that establishes an intermediate layer offering a shared vocabulary that allows specifying restrictions and gives a common semantics for any application which uses Learning Objects metadata. Thus, every change in the LOM ontology will be reflected in the different applications that use this ontology with no need to modify their code. In this work, as a proof of concept, we present an assistant prototype to help users to load these Objects in repositories. This prototype automatically extracts, restricts and validates the Learning Objects metadata using the LOM ontology.

Computational thinking (CT) has been recognized as a collection of understandings and skills required for new generations of students not only proficient at using tools, but also at creating them and understanding the implication of their capabilities and limitations.The objective of this research was to develop a module of solved problems for the development of CT in first-year computer engineering students at the University of Cienfuegos, Cuba. Students depend on current research to understand the definition, function, and culture of CT and to consider how it can improve their analytical and critical skills. A contextualized definition of the CT method is proposed as a cognitive process executed by humans to solve problems using computational concepts. This method improves CT in terms of decomposition, pattern recognition, algorithm design, abstraction, data representation, problem decomposition, algorithmic thinking, and generalization of patterns, simulation and evaluation. The research carried out is an experimental design with a pretest and a posttest, with a control group and an experimental group to which the intervention was applied, both with 18 students. This article describes a study developing and incorporating CT modules and assessing their effect on the comprehension of CT principles by preservation teachers as well as their computing attitude. The results show that the implementation of analytical thought in education courses will successfully affect the comprehension of CT principles by preservice students.

This chapter describes the development of a recommender system of learning objects. This system helps a user to find educational resources that are most appropriate to his/her needs and preferences. The search is performed in different repositories of learning objects, where each object has descriptive metadata. Metadata is used to retrieve objects that satisfy not only the subject of the query, but also the user profile, taking into account his/her characteristics and preferences. A multi-agent architecture that includes several types of agents with different functionalities is used. In this chapter, we describe the modelization of the Personalized Search Agent (PS-Agent) as a graded BDI (Belief-Desire-Intention) agent. This agent is responsible for making a flexible content-based retrieval and provides an ordered list of the resources that better meet the user profile data. A prototype was implemented, and experimentation results are presented.

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