Student-oriented planning of e-learning contents for Moodle

We present a way to automatically plan student-oriented learning contents in Moodle. Rather than offering the same contents for all students, we provide personalized contents according to the students' background and learning objectives. Although curriculum personalization can be faced in several ways, we focus on artificial intelligence (AI) planning as a very useful formalism for mapping actions, i.e. learning contents, in terms of preconditions (precedence relationships) and causal effects to find plans, i.e. learning paths that best fit the needs of each student. A key feature is that the learning path is generated and shown in Moodle in a seamless way for both the teacher and student, respectively. We also include some experimental results to demonstrate the scalability and viability of our approach. & 2015 Elsevier Ltd. All rights reservedThis paper is co-funded with support from the European Commission, the European Social Fund and the Regione Calabria. The paper was also partially funded by the Consolider AT project CSD2007-0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation, the MICINN project TIN2011-27652-C03-01 and the Valencian Prometeo project II/2013/019.Caputi, V.; Garrido Tejero, A. (2015). Student-oriented planning of e-learning contents for Moodle. Journal of Network and Computer Applications. 53:115-127. https://doi.org/10.1016/j.jnca.2015.04.001S1151275

O desenvolvimento de um algoritmo para geração de tarefas individualizadas para o ensino de habilidades básicas de leitura

O presente artigo busca demonstrar os resultados obtidos no desenvolvimento de um algoritmo de geração de tarefas individualizadas para o ensino de habilidades básicas de leitura. Para tal, explicita-se inicialmente o paradigma teórico-pedagógico da Análise do Comportamento no que tange ao ensino e aprendizagem de habilidades básicas de leitura, sendo este o modelo teórico adotado pelo estudo. Atualmente o algoritmo desenvolvido é capaz de adaptar-se a determinados padrões claros de erros e acertos, mas falha em situações mais aleatórias. Propomos como forma de continuidade e melhoria do desenvolvimentodo algoritmo a sua implementação no jogo “As Aventuras de Amaru”, sendo este o ambiente de testeque será utilizado para coleta de dados com crianças em idade escolar e em período de alfabetização

O desenvolvimento de um algoritmo para geração de tarefas individualizadas para o ensino de habilidades básicas de leitura

O presente artigo busca demonstrar os resultados obtidos no desenvolvimento de um algoritmo de geração de tarefas individualizadas para o ensino de habilidades básicas de leitura. Para tal, explicita-se inicialmente o paradigma teórico-pedagógico da Análise do Comportamento no que tange ao ensino e aprendizagem de habilidades básicas de leitura, sendo este o modelo teórico adotado pelo estudo. Atualmente o algoritmo desenvolvido é capaz de adaptar-se a determinados padrões claros de erros e acertos, mas falha em situações mais aleatórias. Propomos como forma de continuidade e melhoria do desenvolvimentodo algoritmo a sua implementação no jogo “As Aventuras de Amaru”, sendo este o ambiente de testeque será utilizado para coleta de dados com crianças em idade escolar e em período de alfabetização

Integration of evolutionary algorithm in an agent-oriented approach for an adaptive e-learning

This paper describes an agent- oriented approach that aims to create learning situations by solving problems. The proposed system is designed as a multi-agent that organizes interfaces, coordinators, sources of information and mobiles. The objective of this approach is to get learners to solve a problem that leads them to get engaged in several learning activities, chosen according to their level of knowledge and preferences in order to ensure adaptive learning and reduce the rate of learner abundance in an e-learning system. The search for learning activities procedure is based on evolutionary algorithms typically: genetic algorithm, to offer learners the optimal solution adapted to their profiles and ensuring a resolution of the proposed learning problem. In terms of results, we have adopted “immigration strategies” to improve the performance of the genetic algorithm. To show the effectiveness of the proposed approach we have made a comparative study with other artificial intelligence optimization methods. We conducted a real experiment with primary school learners in order to test the effectiveness of the proposed approach and to set up its functioning. The experiment results showed a high rate of success and engagement among the learners who followed the proposed adaptive learning scenario

Consumers’ Kansei Needs Clustering Method for Product Emotional Design Based on Numerical Design Structure Matrix and Genetic Algorithms

Consumers’ Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design

Unapređenje sistema za e-učenje semantičkom nadgradnjom

Within this doctoral dissertation an enhancement to e-learning systems has been proposed and the evaluation of its impact in the context of real learning was performed. The proposed enhancement is based on coupling a notion-relation graph, in the RDF format, with the textual learning material, to enable semantic queries over the graph by dragging and droping the words in the text, in cases when the relation between two notions is unclear and still required for the student to continue learning, with the aim of reducing the need for notion definition searches (distractions). To evaluate the poposed enhancement efficiency an e-learning system simulation, learning text and questions were developed. 191 Faculty of Electronic Engineering students took part in the main experiment and 27 took part in the verification experiment that followed. The key results proved the basic hypotheses: the proposed enhancement significantly reduced the participants' need for searching within the text and their completion time, while it raised their preparedness to proceed with learning. Additional results gave insight into other effects of the proposed enhancement and will be used as guidelines for further development. All key results were confirmed by the verification experiment