Proporcionar experiencias de aprendizaje ubicuo mediante la combinación de Internet de las Cosas y los estándares de e-Learning

[ES]Actualmente, el aprendizaje está teniendo lugar con mayor frecuencia en cualquier lugar y en cualquier momento. Esto implica que los ambientes del aprendizaje electrónico se expandan desde los entornos de aprendizaje solo virtuales a entornos que implican espacios físicos. Gracias a la evolución de Internet, las TIC (Tecnologías de la Información y Comunicación) y a la Internet de las Cosas, se pueden experimentar nuevos escenarios de aprendizaje por parte de los estudiantes, ya sea individualmente o en colaboración. Estos escenarios de aprendizaje ubicuos, permiten compaginar tanto ambientes virtuales como ambientes físicos. Por tanto, estas experiencias se caracterizan por las interacciones posibles del estudiante con el entorno físico, la detección de los datos contextuales, y también la adaptación de las estrategias pedagógicas y de los servicios según el contexto. Este artículo pretende aprovechar esta tendencia y sustentarla en las normas existentes de aprendizaje electrónico como IMS LD y LOM. La solución propuesta es extender los modelos de normas de aprendizaje electrónico como IMS LD y LOM para soportar Internet de las Cosas y para aportar un enfoque de adaptación de las actividades de aprendizaje según el contexto del estudiante y su huella digital utilizando la API eXperience. En este contexto y con el fin de permitir las capacidades de razonamiento y la interoperabilidad entre los modelos propuestos se proponen representaciones ontológicas y una implementación de la solución. Además, se plantea una arquitectura técnica que resalta los componentes de software necesarios y sus interacciones. Y, por último, se implementa y se evalúa un escenario de aprendizaje ubicuo

Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

[EN] Knowledge engineering relies on ontologies, since they provide formal descriptions of real¿world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi¿automatically or automatically from scratch. It not only improves the efficiency of the ontology development pro¿ cess but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great poten¿ tial of ontology learning, we present an automatic ontology¿based model evolution approach to ac¿ count for highly dynamic environments at runtime. This approach can extend initial models ex¿ pressed as ontologies to cope with rapid changes encountered in surrounding dynamic environ¿ ments at runtime. The main contribution of our presented approach is that it analyzes heterogene¿ ous semi¿structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology¿based model. Within this approach, we aim to automatically evolve an initial ontology¿based model through the ontology learning approach. Therefore, this approach is illustrated using a proof¿of¿concept implementation that demonstrates the ontology¿based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology¿based models. First, we consider a feature¿based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria¿based eval¿ uation to assess the content of the evolved models. Finally, we perform an expert¿based evaluation to assess an initial and evolved models¿ coverage from an expert¿s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.Jabla, R.; Khemaja, M.; Buendía García, F.; Faiz, S. (2021). Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments. Applied Sciences. 11(22):1-30. https://doi.org/10.3390/app112210770130112

A Hybrid Recommender System for HCI Design Pattern Recommendations

[EN] User interface design patterns are acknowledged as a standard solution to recurring de¿ sign problems. The heterogeneity of existing design patterns makes the selection of relevant ones difficult. To tackle these concerns, the current work contributes in a twofold manner. The first con¿ tribution is the development of a recommender system for selecting the most relevant design pat¿ terns in the Human Computer Interaction (HCI) domain. This system introduces a hybrid approach that combines text¿based and ontology¿based techniques and is aimed at using semantic similarity along with ontology models to retrieve appropriate HCI design patterns. The second contribution addresses the validation of the proposed recommender system regarding the acceptance intention towards our system by assessing the perceived experience and the perceived accuracy. To this pur¿ pose, we conducted a user¿centric evaluation experiment wherein participants were invited to fill pre¿study and post¿test questionnaires. The findings of the evaluation study revealed that the per¿ ceived experience of the proposed system¿s quality and the accuracy of the recommended design patterns were assessed positively.Braham, A.; Khemaja, M.; Buendía García, F.; Gargouri, F. (2021). A Hybrid Recommender System for HCI Design Pattern Recommendations. Applied Sciences. 11(22):1-25. https://doi.org/10.3390/app112210776S125112

Education in the knowledge society : EKS

Resumen basado en el de la publicaciónCon la evolución de Internet y las TIC los estudiantes pueden experimentar nuevos escenarios de aprendizaje (individualmente o en colaboración): el aprendizaje se produce en cualquier lugar y momento. Los ambientes del aprendizaje electrónico se expanden desde entornos solo virtuales a entornos que implican espacios físicos. Son escenarios de aprendizaje ubicuos. Se propone extender los modelos de normas de aprendizaje electrónico (IMS, LD, LOM) para soportar Internet de las Cosas y para aportar un enfoque de adaptación de las actividades de aprendizaje según el contexto del estudiante y su huella digital utilizando la API eXperience. Con el fin de permitir las capacidades de razonamiento y la interoperabilidad entre los modelos propuestos se proponen representaciones ontológicas y una implementación de la solución en un escenario de aprendizaje ubicuo. Además, se plantea una arquitectura técnica que resalta los componentes de software necesarios y sus interacciones.ES

Towards a new requirements’ definition methodology using ontologies for Pervasive Games Based Learning Systems

Since a Pervasive Games Based Learning System (PGBLSs) is considered as technology enhanced learning system, it becomes important to enhance the development process. Despite the growing presence of mobile devices and the wireless network communication technologies, users needs satisfaction is particularly challenging due to problems arising from the highly dynamic environments in which services will operate. We propose, in this paper, a semantic model driven requirements engineering process in order to improve the development of PGBLSs. This model is based on an ontology of requirements (RO) as a powerful formalism to assist requirements' analysts for fulfilling changing requirements in PGBLSs dynamic contexts. In such environments, analysts have to establish the relative priorities of requirements for resolving conflicting requests. For this issue, a requirements analysis technique is also proposed

Ontologie modulaire pour la fabrication et l'exploitation de vêtements intelligents dédiés au sport

National audienc

Balancing Timing and Accuracy Requirements in Human Activity Recognition Mobile Applications

Timing requirements are present in many current context-aware and ambient intelligent applications. These kinds of applications usually demand a timing response according to needs dealing with context changes and user interactions. The current work introduces an approach that combines knowledge-driven and data-driven methods to check these requirements in the area of human activity recognition. Such recognition is traditionally based on machine learning classification algorithms. Since these algorithms are highly time consuming, it is necessary to choose alternative approaches when timing requirements are tight. In this case, the main idea consists of taking advantage of semantic ontology models that allow maintaining a level of accuracy during the recognition process while achieving the required response times. The experiments performed and their results in terms of checking such timing requirements along with keeping acceptable recognition levels confirm this idea as shown in the final section of the work

Dynamic Reconfiguration of Smart Sensors: * A Semantic Web based Approach

International audienceThis paper aims to tackle issues related to the (re-)configuration of Smart Objects. These latter are composed specifically of smart sensors that embed basic sensors, a micro-controller and software snippets as well. In order to make this (re-)configuration feasible at runtime, we propose a semantic Web based approach that relies on a set of ontology modules together with a set of logical rules and reasoning processes to drive the (re-)configuration mechanism while taking into account, at the same time, requirements of the application domain. We validate our approach by means of a prototype that shows relevance of developed ontologies and (re-)configuration mechanism