Fabiano A. Dorça

A multiagent architecture for distance education systems

We describe a multiagent architecture for Web-based distance education systems that presents characteristics of intelligence and adaptability. This architecture is based on techniques of distributed artificial intelligence, planning and intelligent tutoring systems. As a result, the system allows adaptation of a given course to different types of students.

Automatic student modeling in adaptive educational systems through probabilistic learning style combinations: a qualitative comparison between two innovative stochastic approaches

Considering learning and how to improve students’ performances, adaptive educational systems must know the way in which an individual student learns best. In this context, this work presents a comparison between two innovative approaches to automatically detect and precisely adjust students’ learning styles during an adaptive course. These approaches take into account the nondeterministic and nonstationary aspects of learning styles. They are based upon two stochastic techniques: Markov chains and genetic algorithms. We found that the genetic algorithm (GA) based approach detects learning styles earlier and consequently provides personalized content earlier, making the learning process easier. The Markov based approach produces more fine-tuned results, taking into account strengths of learning styles.

Detecção e Correção Automática de Estilos de Aprendizagem em Sistemas Adaptativos para Educação

Um dos aspectos mais importantes em sistemas adaptativos para educacaoe a capacidade de prover personalizacao de acordo com as necessidades especificasde cada estudante. Neste contexto, este trabalho apresenta uma abordagem promissorapara deteccao e correcao automatica de estilos de aprendizagem (EA) baseadaem cadeias de Markov. A maioria dos trabalhos nesta area apresentam abordagenscomplexas e ineficientes em algum aspecto. Alem disto, a abordagem apresentadaneste trabalho tem como vantagem tornar possivel aos estudantes o desenvolvimentode novas capacidades cognitivas, sendo baseada na combinacao de estilos de aprendizagem(CEA) e na correcao dinâmica de possiveis inconsistencias no modelo do estudante(ME), levando em consideracao o forte aspecto nao-deterministico do processode ensino-aprendizagem. Resultados promissores foram obtidos nos testes realizadoscom esta abordagem e sao discutidos neste trabalho.

An Automatic and Dynamic Student Modeling Approach for Adaptive and Intelligent Educational Systems Using Ontologies and Bayesian Networks

Dynamic adaptation of educational content has been an important research topic. Therefore, in order for it to run effectively, student models that properly describe and monitor the cognitive state of students are needed. In this sense, this paper presents a hybrid student model approach that combines ontologies and Bayesian Networks to identify the knowledge of each student based on their characteristics and behavior while using an Adaptive Educational System. Experiments were performed with real student participants in a higher education course using an experimental prototype developed to verify the viability of the approach, which showed satisfactory results.

Implementation and use of Simulated Students for Test and Validation of new Adaptive Educational Systems: a Practical Insight

Studies attest that learning is facilitated if teaching strategies are in accordance with students learning styles, making learning process more effective and considerably improving students performances. In this context, one major research point – and a challenge – is to efficiently discover students’ learning styles. But, the test and validation of new approaches in this field requires substantial amounts of financial, human resources (tutors and students) and time. In this way, the use of simulated students for test and validation of new approaches in this field is very important. Therefore, this work depicts the implementation and use of simulation for empirical evaluation of three different strategies for automatic learning styles modelling. It was necessary to compare the efficiency of the strategies, in order to choose the best one. Therefore, it was needed a practical mechanism to evaluate and compare them, preferably without the engagement of human resources. Then, the main goal of using simulation in this work is to compare strategies’ efficiency and to discover the most promising one. The research was empirical, and has led to considerable enhancements on an intelligent component for automatic modelling of learning and teaching styles, which has been tested, adjusted and improved in a reasonable time and with low cost. The best strategy was clearly found, as depicted by experiments and results presented in this paper.

Learning Objects Authoring Supported by Ubiquitous Learning Environments

Learning objects authoring is still a complex and time-consuming task for instructors, which requires attention to technical and pedagogical aspects. However, one can take advantage of the Ubiquitous Learning Environments characteristics to make it a mild process by means of automatic or semi-automatic processes. In this way, this paper presents an approach for creating learning objects and their metadata in such environments considering collaborative interactions among users. The proposed approach is being integrated to a real multimedia capture system used as a complementary tool in a university.

Consistent Evolution of Student Models by Automatic Detection of Learning Styles

One of the most important features of adaptative e-learning systems is the personalisation according to specific requirements of each individual student. In considering learning and how to improve student learning, these systems must know the way in which an individual learns. In this context, we introduce a new approach for consistent evolution of student models by automatic detection of student learning styles. Most of the work in this field presents complex and inefficient approachs. Our approach is based on learning styles combination and dynamic correction of inconsistencies in the student model, taking into account the non-deterministic aspect of the learning process. Promising results were obtained from tests, and some of them are discussed in this paper.

A New Approach to Discover Students Learning Styles in Adaptive Educational Systems

A educacao a distancia tem ganhado significativa atencao tanto na academia quanto nas iniciativas governamentais. Neste contexto, cresce tambem a preocupacao com a avaliacao da qualidade dos diversos aspectos destes cursos mediados pelas tecnologias da informacao e comunicacao. Apesar de muitos trabalhos discutirem diversos aspectos da avaliacao em Ead, a literatura carece de relatos de experiencia que, especialmente, abordem os cursos tecnicos a distancia que possuem especificidades relevantes. Assim, este trabalho relata uma experiencia onde se avalia e adapta uma das propostas existentes na literatura de avaliacao mediada por foruns, ao contexto dos cursos tecnicos a distancia, constituindo-se esta adaptacao e sua discussao as principais contribuicoes do mesmo.Em Educacao a Distância mediada por meio de Ambientes Virtuais de Aprendizagem, foruns de discussao sao um instrumento importante e amplamente utilizado na articulacao de debates e discussoes entre os atores envolvidos no processo de ensino e aprendizagem. Com a ampla utilizacao dos foruns muitas mensagens sao trocadas e isso, por vezes, excede a capacidade de monitoramento por parte dos professores e tutores. O presente trabalho apresenta a concepcao de um classificador de mensagens de foruns que classifica as mensagens em positivas ou negativas, a fim de identificar mensagens que necessitam de maior atencao. Este trabalho aplica conceitos de mineracao de textos, com o algoritmo SVM obtendo taxas de acerto satisfatorias.Este artigo apresenta o framework Contagious, cujo proposito e estabelecer diretrizes que norteiem a construcao de redes sociais online orientadas a Difusao de Inovacoes. Compreendo-se o fenomeno das redes sociais online como consequencia natural do carater social do ser humano, vislumbrou-se esse meio tecnologico de comunicacao e interacao social como potencial ferramenta para a extensao de praticas educativas, com vistas a formacao do carater integral do cidadao. Para isso, foi adotada a teoria de Difusao de Inovacoes, propria das ciencias sociais. As contribuicoes deste trabalho, portanto, compreendem duas vertentes: a) o mapeamento de principios de uma teoria social na forma de recursos computacionais e; b) um enfoque orientado a educacao sobre as redes sociais online.A proposta do trabalho consiste em desenvolver um sistema para ser usado no celular como ferramenta de auxilio para alfabetizacao, utilizando-se de imagens e sons como forma de facilitar o aprendizado. Como metodo de desenvolvimento utiliza-se o processo P@PSEduc (Processo Agil para Software Educativo) e a ferramenta JME (Java Micro Edition).O crescente uso e difusao de tecnologias Web, a ubiquidade de ferramentas educacionais vem proporcionado verdadeiras revolucoes nos ambientes de ensino. Atualmente, sabe-se que nao mais se deve tratar alunos de forma homogenea, como se assim os fossem. Em face disso, este artigo apresenta um sistema adaptativo de apoio a aprendizagem colaborativa, cujo tema e a construcao e representacao do conhecimento por meio de mapas mentais multimidia. Tal sistema, baseia-se na Teoria da Carga Cognitiva, cuja preocupacao primaria e a facilidade com a qual as informacoes sao processadas pelos individuos.

An Approach for Automatic and Dynamic Analysis of Learning Objects Repositories through Ontologies and Data Mining Techniques for Supporting Personalized Recommendation of Content in Adaptive and Intelligent Educational Systems

Adaptive and Intelligent Educational Systems are interesting resources for supporting teaching-learning activities. Such environments make use of intelligent techniques to adapt educational content to the real needs of students. With the increasing amount of educational content becoming available, there is a good reason to believe that smart data analysis and machine learning techniques will become indispensable ingredients for educational progress. Therefore, this work proposes an approach for automatic and dynamic analysis of learning objects repositories in which an ontology models the relations between learning objects attributes and learning styles. Promising results have been obtained, and they are presented in this work.

An Automatic and Dynamic Knowledge Assessment Module for Adaptive Educational Systems

Adaptive Educational Systems (AES) make use of Artificial Intelligence techniques aiming at adapting themselves to the real needs of the student, and through such provide a personalized and individualized teaching. In order for this adaptation to be successful, it is important that the system knows the level of knowledge concerning the real cognitive state of the students. In this manner, this article presents an approach for predicting academic performance based on ontologies and Bayesian networks. A knowledge assessment module was developed and integrated to a Ubiquitous Learning Environment (ULE) in real and actual use. During three semesters, five graduate classes were analyzed with the intent of verifying the correlation between the grades generated by the module and the actual grades obtained by the students. It was noted that 80% of the analyzed samples obtained very high or substantial correlations. This indicates that the proposed module is adequate for identifying and predicting academic performance.