Competency-Based Learning Object Sequencing Using Particle Swarms

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LEARNING OBJECT. DEFINITION AND CLASSIFICATION

[EN] The current trend in higher education includes competencies in the curricula. This integration can be done through the competency-based learning. The competence is acquired through various learning objects to be achieved. In this paper different dimensions to define a learning object (LO) and different classifications associated to them have been proposed. An analysis and synthesis of the results obtained have been presented.Alarcón Valero, F.; Alemany Díaz, MDM.; Boza, A.; Cuenca, L.; Gordo Monzó, ML.; Fernández-Diego, M.; Ruiz Font, L. (2015). LEARNING OBJECT. DEFINITION AND CLASSIFICATION. EDULEARN Proceedings (Internet). 4479-4488. http://hdl.handle.net/10251/95287S4479448

PERSONALIZED LEARNING PATH GENERATION BASED ON GENETIC ALGORITHMS

A substantial disadvantage of traditional learning is that all students follow the same curriculum sequence, but not all of them have the same background of knowledge, the same preferences, the same learning goals, and the same needs. Traditional teaching resources, such as textbooks, in most cases orient students to follow fixed sequences during the learning process, thus impairing their performance. Curriculum sequencing is an important research issue for learning process because no fixed learning paths will be appropriate for all learners. For this reason, many research papers are focused on the development of mechanisms to offer personalization on learning sequences, considering the learner needs, interests, behaviors, and abilities. In most cases, these researches are totally focused on the student\u27s preferences, ignoring the level of difficulty and the relation degree that exists between various concepts in a course. This work presents a genetic algorithm-based model to offer personalization on learning paths, considering the level of difficulty and relation degree of the constituent concepts of a course. The experimental result shows that the genetic algorithm is suitable to generate optimal learning paths based on learning object difficulty level, duration, rating, and relation degree between each learning object. Furthermore, it indicates that using the proposed genetic-based approach for personalized learning path generation is superior to the traditional curriculum sequencing

An Automated Adaptive Mobile Learning System Using Optimal Shortest Path Algorithms

Technological innovation opens the door to create a personal learning experience for any student. In this research, we discuss adaptive learning techniques and the style of learning that integrates existing learning techniques combined with new ideas. To create an effective user friendly learning environment, adaptive learning techniques should be used in order to identify the personal needs of students and reduce their individual knowledge gaps. The result will produce learning path containing relevant content that will provide a better learning direction for each student. This dissertation explores the opportunity of using adaptive learning techniques to identify the personal needs of each student by combining different learning styles, student profiles and individualized course content. By using a directed graph, we are able to represent an accurate picture of the course descriptions for online courses through computer-based implementation of various educational systems. E-learning (electronic learning) and m-learning (mobile learning) systems are modeled as a weighted directed graph where each node represents a course unit. The Learning Path Graph represents and describes the structure of the domain knowledge, including the learning goals, and all other available learning paths. In this research, we propose a system prototype that implements optimal adaptive learning path algorithms using students’ information from their profiles and their learning style. Our goal is to improve students’ learning performances through the m-learning system in order to provide suitable course contents sequenced in a dynamic form for each student

Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks

Tesis por compendio[EN] The long-lasting trend in the field of computation of stress and resource distribution has found its way into computer networks via the concept of peer-to-peer (P2P) connectivity. P2P is a symmetrical model, where each network node is enabled a comparable range of capacities and resources. It stands in a stark contrast to the classical, strongly asymmetrical client-server approach. P2P, originally considered only a complimentary, server-side structure to the straightforward client-server model, has been shown to have the substantial potential on its own, with multiple, widely known benefits: good fault tolerance and recovery, satisfactory scalability and intrinsic load distribution. However, contrary to client-server, P2P networks require sophisticated solutions on all levels, ranging from network organization, to resource location and managing. In this thesis we address one of the key issues of P2P networks: performing efficient resource searches of semantic nature under realistic, dynamic conditions. There have been numerous solutions to this matter, with evolutionary, stigmergy-based, and simple computational foci, but few attempt to resolve the full range of challenges this problem entails. To name a few: real-life P2P networks are rarely static, nodes disconnect, reconnect and change their content. In addition, a trivial incorporation of semantic searches into well-known algorithms causes significant decrease in search efficiency. In our research we build a solution incrementally, starting with the classic Ant Colony System (ACS) within the Ant Colony Optimization metaheuristic (ACO). ACO is an algorithmic framework used for solving combinatorial optimization problems that fits contractually the problem very well, albeit not providing an immediate solution to any of the aforementioned problems. First, we propose an efficient ACS variant in structured (hypercube structured) P2P networks, by enabling a path-post processing algorithm, which called Tabu Route Optimization (TRO). Next, we proceed to resolve the issue of network dynamism with an ACO-compatible information diffusion approach. Consequently, we attempt to incorporate the semantic component of the searches. This initial approximation to the problem was achieved by allowing ACS to differentiate between search types with the pheromone-per-concept idea. We called the outcome of this merger Routing Concept ACS (RC-ACS). RC-ACS is a robust, static multipheromone implementation of ACS. However, we were able to conclude from it that the pheromone-per-concept approach offers only limited scalability and cannot be considered a global solution. Thus, further progress was made in this respect when we introduced to RC-ACS our novel idea: dynamic pheromone creation, which replaces the static one-to-one assignment. We called the resulting algorithm Angry Ant Framework (AAF). In AAF new pheromone levels are created as needed and during the search, rather than prior to it. The final step was to enable AAF, not only to create pheromone levels, but to reassign them to optimize the pheromone usage. The resulting algorithm is called EntropicAAF and it has been evaluated as one of the top-performing algorithms for P2P semantic searches under all conditions.[ES] La popular tendencia de distribución de carga y recursos en el ámbito de la computación se ha transmitido a las redes computacionales a través del concepto de la conectividad peer-to-peer (P2P). P2P es un modelo simétrico, en el cual a cada nodo de la red se le otorga un rango comparable de capacidades y recursos. Se trata de un fuerte contraste con el clásico y fuertemente asimétrico enfoque cliente-servidor. P2P, originalmente considerado solo como una estructura del lado del servidor complementaria al sencillo modelo cliente-servidor, ha demostrado tener un potencial considerable por sí mismo, con múltiples beneficios ampliamente conocidos: buena tolerancia a fallos y recuperación, escalabilidad satisfactoria y distribución de carga intrínseca. Sin embargo, al contrario que el modelo cliente-servidor, las redes P2P requieren de soluciones sofisticadas a todos los niveles, desde la organización de la red hasta la gestión y localización de recursos. Esta tesis aborda uno de los problemas principales de las redes P2P: la búsqueda eficiente de recursos de naturaleza semántica bajo condiciones dinámicas y realistas. Ha habido numerosas soluciones a este problema basadas en enfoques evolucionarios, estigmérgicos y simples, pero pocas han tratado de resolver el abanico completo de desafíos. En primer lugar, las redes P2P reales son raramente estáticas: los nodos se desconectan, reconectan y cambian de contenido. Además, la incorporación trivial de búsquedas semánticas en algoritmos conocidos causa un decremento significativo de la eficiencia de la búsqueda. En esta investigación se ha construido una solución de manera incremental, comenzando por el clásico Ant Colony System (ACS) basado en la metaheurística de Ant Colony Optimization (ACO). ACO es un framework algorítmico usado para búsquedas en grafos que encaja perfectamente con las condiciones del problema, aunque no provee una solución inmediata a las cuestiones mencionadas anteriormente. En primer lugar, se propone una variante eficiente de ACS para redes P2P estructuradas (con estructura de hipercubo) permitiendo el postprocesamiento de las rutas, al que hemos denominado Tabu Route Optimization (TRO). A continuación, se ha tratado de resolver el problema del dinamismo de la red mediante la difusión de la información a través de una estrategia compatible con ACO. En consecuencia, se ha tratado de incorporar el componente semántico de las búsquedas. Esta aproximación inicial al problema ha sido lograda permitiendo al ACS diferenciar entre tipos de búsquedas através de la idea de pheromone-per-concept. El resultado de esta fusión se ha denominado Routing Concept ACS (RC-ACS). RC-ACS es una implementación multiferomona estática y robusta de ACS. Sin embargo, a partir de esta implementación se ha podido concluir que el enfoque pheromone-per-concept ofrece solo escalabilidad limitada y que no puede ser considerado una solución global. Por lo tanto, para lograr una mejora a este respecto, se ha introducido al RC-ACS una novedosa idea: la creación dinámica de feromonas, que reemplaza la asignación estática uno a uno. En el algoritmo resultante, al que hemos denominado Angry Ant Framework (AAF), los nuevos niveles de feromona se crean conforme se necesitan y durante la búsqueda, en lugar de crearse antes de la misma. La mejora final se ha obtenido al permitir al AAF no solo crear niveles de feromona, sino también reasignarlos para optimizar el uso de la misma. El algoritmo resultante se denomina EntropicAAF y ha sido evaluado como uno de los algoritmos más exitosos para las búsquedas semánticas P2P bajo todas las condiciones.[CA] La popular tendència de distribuir càrrega i recursos en el camp de la computació s'ha estès cap a les xarxes d'ordinadors a través del concepte de connexions d'igual a igual (de l'anglès, peer to peer o P2P). P2P és un model simètric on cada node de la xarxa disposa del mateix nombre de capacitats i recursos. P2P, considerat originàriament només una estructura situada al servidor complementària al model client-servidor simple, ha provat tindre el suficient potencial per ella mateixa, amb múltiples beneficis ben coneguts: una bona tolerància a errades i recuperació, una satisfactòria escalabilitat i una intrínseca distribució de càrrega. No obstant, contràriament al client-servidor, les xarxes P2P requereixen solucions sofisticades a tots els nivells, que varien des de l'organització de la xarxa a la localització de recursos i la seua gestió. En aquesta tesi s'adreça un dels problemes clau de les xarxes P2P: ser capaç de realitzar eficientment cerques de recursos de naturalesa semàntica sota condicions realistes i dinàmiques. Existeixen nombroses solucions a aquest tema basades en la computació simple, evolutiva i també basades en l'estimèrgia (de l'anglès, stigmergy), però pocs esforços s'han realitzat per intentar resoldre l'ampli conjunt de reptes existent. En primer lloc, les xarxes P2P reals són rarament estàtiques: els nodes es connecten, desconnecten i canvien els seus continguts. A més a més, la incorporació trivial de cerques semàntiques als algorismes existents causa una disminució significant de l'eficiència de la cerca. En aquesta recerca s'ha construït una solució incremental, començant pel sistema clàssic de colònia de formigues (de l'anglés, Ant Colony System o ACS) dins de la metaheurística d'optimització de colònies de formigues (de l'anglès, Ant Colony Optimization o ACO). ACO és un entorn algorísmic utilitzat per cercar en grafs i que aborda el problema de forma satisfactòria, tot i que no proveeix d'una solució immediata a cap dels problemes anteriorment mencionats. Primer, s'ha proposat una variant eficient d'ACS en xarxes P2P estructurades (en forma d'hipercub) a través d'un algorisme de processament post-camí el qual s'ha anomenat en anglès Tabu Route Optimization (TRO). A continuació, s'ha procedit a resoldre el problema del dinamisme de les xarxes amb un enfocament de difusió d'informació compatible amb ACO. Com a conseqüència, s'ha intentat incorporar la component semàntica de les cerques. Aquest enfocament inicial al problema s'ha realitzat permetent a ACS diferenciar entre tipus de cerques amb la idea de ''feromona per concepte'', i s'ha anomenat a aquest producte Routing Concept ACS o RC-ACS. RC-ACS és una implementació multi-feromona robusta i estàtica d'ACS. No obstant, s'ha pogut concloure que l'enfocament de feromona per concepte ofereix només una escalabilitat limitada i no pot ser considerada una solució global. En aquest respecte s'ha realitzat progrés posteriorment introduint una nova idea a RC-ACS: la creació dinàmica de feromones, la qual reemplaça a l'assignació un a un de les mateixes. A l'algorisme resultant se l'ha anomenat en anglès Angry Ant Framework (AAF). En AAF es creen nous nivells de feromones a mesura que es necessiten durant la cerca, i no abans d'aquesta. El progrés final s'ha aconseguit quan s'ha permès a AAF, no sols crear nivells de feromones, sinó reassignar-los per optimitzar la utilització de feromones. L'algorisme resultant s'ha anomenat EntropicAAF i ha sigut avaluat com un dels algorismes per a cerques semàntiques P2P amb millors prestacions.Krynicki, KK. (2016). Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61293TESISPremios Extraordinarios de tesis doctoralesCompendi

Modelo de seleção dinâmica de objetos de aprendizagem baseado em agentes

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2015.Este trabalho descreve um modelo que possibilita o oferecimento de experiências de aprendizagem na educação on-line, baseado no paradigma de Sistemas Multiagente (SMA), com o intuito de facilitar ouso de Objetos de Aprendizagem (OA) de forma adaptativa em sistemas de gerenciamento de aprendizagem (LMS), bem como de favorecer o reuso de materiais instrucionais que sigam padrões de referência. O modelo proposto estende o conceito de Objetos Inteligentes de Aprendizagem (OIA), através da utilização de uma arquitetura de agentes BDI (Belief, Desire, Intention), sendo capaz de se comunicar com os elementos que constituem o OA, de acordo com um padrão de empacotamento e sequenciamento de objetos. O modelo teórico proposto possibilita a seleção dinâmica de recursos instrucionais com base em informações do LMS, dos metadados do OA,e do desempenho obtido pelo estudante durante a interação com o objeto. Esses elementos do modelo de dados são usados no processo de raciocínio dos agentes, possibilitando o desenvolvimento de experiências de aprendizagem aprimoradas, e com adaptatividade dinâmica. Além disso, a integração com um LMS favorece o reuso de recursos educacionais que são desenvolvidos segundo padrões de referência, e que podem ficar acessíveis ao processo de adaptação automaticamente, logo que forem incluídos no repositório de objetos, sem a necessidade de reconfigurar a estrutura do curso. Para instanciar o modelo teórico, foi desenvolvido um protótipo compatível com objetos de aprendizagem segundo o padrão de integração SCORM e cujos metadados sigam o padrão IEEE-LOM. Este protótipo integra o ambiente de agentes (denominado ILOMAS) ao LMS Moodle, permitindo o acesso aos OIA de forma integrada a cursos reais de instituições que se utilizem da plataforma Moodle. O protótipo foi desenvolvido com base em um subconjunto do modelo teórico, com o intuito de permitir a validação do sistema proposto, do ponto de vista computacional, através de simulações. Conforme verificado na avaliação deste protótipo, obteve-se adaptatividade e reuso. Por fim, apontou-se possibilidades de evolução do modelo, principalmente com a integração à busca semântica de objetos de aprendizagem com base em ontologias.Abstract : This work describes a model that enables the offering of learning experiences in online education, based on the paradigm of Multi-Agent Systems (MAS), in order to facilitate the use of Learning Objects (LO) adaptively in learning management systems (LMS) as well as to encourage the reuse of instructional materials that follow standards. The proposed model extends the concept of intelligent learning objects (ILO), by using a BDI agents' architecture (Belief, Desire, Intention), being able to communicate with the LO's elements, according to apackaging and sequencing standard. The proposed theoretical model enables dynamic selection of instructional resources based on information from the LMS, the learning object metadata, and the performance achieved by the student during the interaction with the LO. These elements of the data model are used in theagent's reasoning process, enabling the development of improved learning experiences, and dynamic adaptivity. Furthermore, the integration with an LMS promotes reuse of educational resources that are developed following reference standards, enabling their access in the adaptation process automatically, as soon as they are included in the LO repository, without having to reconfigure the course structure. To instantiate the theoretical model, it was developed a prototype compliant with SCORM standard LO and whose metadata follow the IEEE-LOM standard. This prototype integrates the agent environment (called ILOMAS) to LMS Moodle, allowing access to the ILO seamlessly to real courses of institutions that use Moodle platform. The prototype was developed based on a subset of the theoretical model, in order to enable the computational validation of the proposed system, through simulations. As verified on this prototype's evaluation, adaptivity and reuse were obtained. Finally, evolution opportunities to the model was pointed, especially with the integration of semantic search for learning objects based on ontologies