Does the Use of Learning Management Systems With Hypermedia Mean Improved Student Learning Outcomes?

Learning management systems (LMSs) that incorporate hypermedia Smart Tutoring Systems and personalized student feedback can increase self-regulated learning (SRL), motivation, and effective learning. These systems are studied with the following aims: (1) to verify whether the use of LMS with hypermedia Smart Tutoring Systems improves student learning outcomes; (2) to verify whether the learning outcomes will be grouped into performance clusters (Satisfactory, Good, and Excellent); and (3) to verify whether those clusters will group together the different learning outcomes assessed in four different evaluation procedures. Use of the LMS with hypermedia Smart Tutoring Systems was studied among students of Health Sciences, all of whom had similar test results in the use of metacognitive skills. It explained 38% of the variance in student learning outcomes in the evaluation procedures. Likewise, three clusters that grouped the learning outcomes in relation to the variable ‘Use of an LMS with hypermedia Smart Tutoring Systems vs. No use’ explained 60.4% of the variance. Each cluster grouped the learning outcomes in the different evaluation procedures. In conclusion, LMS with hypermedia Smart Tutoring Systems in Moodle increased the effectiveness of student learning outcomes, above all in the individual quiz-type tests. It also facilitated personalized learning and respect for the individual pace of student-learning. Hence, modules for the analysis of supervised, unsupervised and multivariate learning should be incorporated into the Moodle platform to provide teaching tools that will undoubtedly contribute to improvements in student learning outcomes.The Research Funding Program 2018 of the Vice-Rectorate for Research and Knowledge Transfer of the University of Burgos

Context-Aware and Adaptable eLearning Systems

The full text file attached to this record contains a copy of the thesis without the authors publications attached. The list of publications that are attached to the complete thesis can be found on pages 6-7 in the thesis.This thesis proposed solutions to some shortcomings to current eLearning architectures. The proposed DeLC architecture supports context-aware and adaptable provision of eLearning services and electronic content. The architecture is fully distributed and integrates service-oriented development with agent technology. Central to this architecture is that a node is our unit of computation (known as eLearning node) which can have purely service-oriented architecture, agent-oriented architecture or mixed architecture. Three eLeaerning Nodes have been implemented in order to demonstrate the vitality of the DeLC concept. The Mobile eLearning Node uses a three-level communication network, called InfoStations network, supporting mobile service provision. The services, displayed on this node, are to be aware of its context, gather required learning material and adapted to the learner request. This is supported trough a multi-layered hybrid (service- and agent-oriented) architecture whose kernel is implemented as middleware. For testing of the middleware a simulation environment has been developed. In addition, the DeLC development approach is proposed. The second eLearning node has been implemented as Education Portal. The architecture of this node is poorly service-oriented and it adopts a client-server architecture. In the education portal, there are incorporated education services and system services, called engines. The electronic content is kept in Digital Libraries. Furthermore, in order to facilitate content creators in DeLC, the environment Selbo2 was developed. The environment allows for creating new content, editing available content, as well as generating educational units out of preexisting standardized elements. In the last two years, the portal is used in actual education at the Faculty of Mathematics and Informatics, University of Plovdiv. The third eLearning node, known as Agent Village, exhibits a purely agent-oriented architecture. The purpose of this node is to provide intelligent assistance to the services deployed on the Education Pportal. Currently, two kinds of assistants are implemented in the node - eTesting Assistants and Refactoring eLearning Environment (ReLE). A more complex architecture, known as Education Cluster, is presented in this thesis as well. The Education Cluster incorporates two eLearning nodes, namely the Education Portal and the Agent Village. eLearning services and intelligent agents interact in the cluster

Propuesta de arquitectura y construcción de aprendizaje automático (AA) como estrategia para la reducción de los niveles de deserción universitaria debido a factores académicos

Introduction:  Machine Learning arises as one of the techniques of artificial intelligence, with the development of computer programs that, through algorithms, access data and use them to learn and predict results. Their application in education allows for the characterization of problems or difficulties in learning through the analysis of student performance. Objective:  Identification of applications of Machine Learning that can be applied to the educational field accompanied by a proposal of architecture for the application in an environment of personalized education. Methodology: This article begins with the review of the literature on the characteristics of Machine Learning and academic desertion, with an emphasis on the Colombian case, the Hyper-personalization and its applicability to learning methodologies. Then, a proposal of architecture in a Machine Learning environment is generated in order to mitigate the academic desertion caused by academic factors. Finally, we propose mechanisms for evaluating the proposed architecture, with a subsequent synthesis and discussion of the results. Conclusions: The construction of a Moodle architecture for the hyper-personalization of learning, is a global perspective of the representative factors proposed for the development of applications through Machine Learning. This could lead to a decrease in levels of university academic desertion because it facilitates the management of knowledge, information and adaptation through the analysis of scenarios. Originality: The proposed architecture is shown as an application of machine learning in social cases such as academic desertion, allowing the inclusion of automatic learning models with the requirements of an educational environment. Restrictions: The case for the application for the Hyper-personalization of learning uses an academic approach which can generate invalid results regarding desertion levels.Introducción: El Machine Learning, surge como una de las técnicas de la inteligencia artificial, en la cual, a través de algoritmos, accede a los datos y los utiliza para aprender y predecir resultados. En cuanto su aplicación en la educación permite la caracterización de dificultades en el aprendizaje a través del análisis de su rendimiento. Objetivo: Identificación de aplicaciones del Machine Learning aplicado al ámbito educativo que permitan la disminución de los niveles de deserción académica, a través de una propuesta de arquitectura para su aplicación en un entorno de educación personalizada. Metodología: Se inicia con la revisión de la literatura sobre las características del aprendizaje automático, la deserción académica, con énfasis en el caso colombiano, la hiperpersonalización y su aplicabilidad a las metodologías de aprendizaje; generando a continuación una propuesta de arquitectura en un entorno de Aprendizaje Automático, con el fin de mitigar la deserción académica provocada por factores académicos. Finalmente, se proponen mecanismos de evaluación de la arquitectura propuesta, con una posterior síntesis y discusión de los resultados. Conclusiones: La construcción de una arquitectura del Moodle de Hiperpersonalización del aprendizaje, es una perspectiva global de los factores representativos propuestos para el desarrollo de aplicaciones a través del Machine Learning, lo cual podría llevar a la disminución de los niveles de deserción académica universitaria, en el sentido en que se facilita la gestión del conocimiento, la información y la adaptación a través del análisis de escenarios. Originalidad: La arquitectura propuesta se muestra como una aplicación del Machine Learning en casos de tipo social como la deserción académica, permitiendo la inclusión del modelado de aprendizaje automático con los requerimientos de un entorno educativo. &nbsp