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

Ferreira, Hiran; Brant-Ribeiro, Taffarel; Araújo, Rafael Dias; Dorça, Fabiano; Cattelan, Renan G.

doi:10.1109/ictai.2016.0116

Cited by 18 publications

(12 citation statements)

References 26 publications

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“…This article extends the approach proposed in Ferreira et al, (2016;2017a, 2017b. OSM-V allow students and instructors to assess performance-related information in educational systems.…”

Section: Osm-v: An Open Student Model For Assessment Visualizationmentioning

confidence: 96%

“…Classroom eXperience (CX) is a smart learning environment with content recommendation and personalization capabilities (Araújo et al, 2013;Dorça et al, 2016;Ferreira et al, 2017a), semantic (Ferreira et al, 2016), social and collaborative features (Araújo et al, 2017). It comprises a multimedia capture platform for automatically recording lectures in a classroom equipped with ubiquitous computational devices, such as electronic whiteboards, microphones, video cameras, and multimedia projectors -an infrastructure common today in many schools and universities.…”

Section: Classroom Experiencementioning

confidence: 99%

“…Most of these studies, however, do not exploit intelligent techniques for processing and structuring the processed information, allowing only the presentation of the content. A key benefit of our approach lies precisely in the fact that it explores intelligent strategies to deal with uncertainties, through joint probability structures using Bayesian Networks, and through semantic and ontological resources for the proper representation and processing of inferences (Ferreira et al 2016;2017b). Based on this, our approach presents an important advance for the state of the art, allowing the definition of an architecture based on intelligent tools that explores the capabilities of OSMs.…”

Section: Related Workmentioning

confidence: 99%

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Technology-enhanced assessment visualization for smart learning environments

Ferreira

Oliveira

Araújo

et al. 2019

Smart Learn. Environ.

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In Smart Learning Environments, students need to be aware of their academic performance so they can self-regulate their learning process. Likewise, the teaching process can also be improved if instructors are able to supervise the progress of students, both individually and globally, and anticipate proper pedagogical strategies. Thus, effective Student Models, capable of identifying and predicting the level of knowledge of students, are a key requirement in modern educational systems. In this article, we revisit OSM-V, an Open Student Model with Information Visualization capabilities that allow students and instructors to assess performance-related information in educational systems. We detail its architecture and how it was integrated into Classroom eXperience, a Smart Learning Environment with multimedia capture capabilities. We also present extended results from experiments that evaluate both the perception of utility and behavioral changes in students who used OSM-V, showing that it can positively impact students' learning and positively influence their study habits.

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“…This article extends the approach proposed in Ferreira et al, (2016;2017a, 2017b. OSM-V allow students and instructors to assess performance-related information in educational systems.…”

Section: Osm-v: An Open Student Model For Assessment Visualizationmentioning

confidence: 96%

Section: Classroom Experiencementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Technology-enhanced assessment visualization for smart learning environments

Ferreira

Oliveira

Araújo

et al. 2019

Smart Learn. Environ.

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“…Para processar essas inferências, regras SWRL foram criadas, a fim de proporcionar um resultado mais condizente com as reais capacidades dos estudantes, pois além de analisarem o conhecimento, também consideram aspectos comportamentais (nível de colaboração, nível de gamificação, atividades desenvolvidas, etc. iii Context Representação do contexto de acesso [Rezende et al 2015] iii Device Dispositivo usado pelo estudante [Verbert et al 2012, Rezende et al 2015 iii Location Localização do estudante durante o acesso [Verbert et al 2012, Rezende et al 2015 iv Knowledge State Estado de conhecimento do estudante [Nguyen et al 2011] As regras propostas para esse modelo baseiam-se nas informações representadas pela ontologia combinadas com os valores de inferência já realizados por um modelo probabilístico [Ferreira et al 2016] e produzem um resultado que indica o nível de desempenho do estudante. Um detalhe interessante deste modeloé justamente a capacidade de extensão e inclusão de novas regras, visto que cada domínio traz suas próprias características e particularidades.…”

Section: Base De Regrasunclassified

Uma Abordagem Baseada em Ontologias para Modelagem e Avaliação do Estudante em Sistemas Adaptativos e Inteligentes para Educação

Ferreira¹,

Araújo²,

Dorça³

et al. 2017

Anais Do XXVIII Simpósio Brasileiro De Informática Na Educação (SBIE 2017)

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Abstract. New technologies emerge everyday with the aim of supporting the development of tools that are more adaptable to the particular needs of each user. One of the computational resources that strives to improve these processes are Adaptive and Intelligent Educational Systems (AIES)s, which possess the capacity to alter its characteristics in order to attend to the users' needs. This paper proposes an ontology-based approach for student modeling and assessment in AIESs. The main function of this model is to follow the activities developed by the student and to determine a level of performance based on their knowledge and behavior. Experiments with real students were developed, in which was possible to identify the main indicators to evaluate and monitor their progress during the use of a virtual environment.Resumo. Novas tecnologias surgem diariamente com o intuito de auxiliar na criação de ferramentas cada vez mais adaptáveisàs particularidades de cada usuário. Um dos recursos computacionais que busca melhorar esses processos são os Sistemas Adaptativos e Inteligentes para Educação (SAIE)s, que possuem a capacidade de alterar suas características para atenderàs necessidades individuais de cada estudante. Este artigo propõe uma abordagem baseada em ontologias para a modelagem e avaliação do estudante em SAIEs. A principal função desse modeloé acompanhar as atividades desenvolvidas pelo estudante e determinar um nível de desempenho com base no seu conhecimento e comportamento. Experimentos com estudantes reais foram desenvolvidos, no qual foi possível identificar os principais indicadores para avaliar e acompanhar o seu progresso durante a utilização de um ambiente virtual. IntroduçãoNosúltimos anos um grande número de sistemas adaptativos tem surgido com o intuito de auxiliar e apoiar usuários na realização de suas atividades. Esses sistemas, em sua maioria, baseiam-se em técnicas inteligentes para se adaptaràs reais necessidades de cada usuário. Um dos cenários que vêm ganhando destaque para a aplicação desses conceitos são os ambientes educacionais. Quando aplicado de forma efetiva, esses ambientes educacionais que recebem características adaptativas tornam-se mais adequados para auxiliar o processo de ensino/aprendizagem [Brusilovsky 2001].

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“…These models are mostly in a state of knowledge of learners, learning styles or meta-cognitive characteristics as a reference, using different modeling methods to build models, so as to realize individualized teaching. For example: Dynamic Bayesian networks are used to construct student models to represent multiple skills in one model [1] . Ciolacu et al (2018) used an early recognition system based on machine learning algorithms to predict the final grades of students before the final exam compared with foreign countries, the research on the construction of personalized student model in China is based on the reference of foreign literature, mainly for three aspects.…”

mentioning

confidence: 99%

Construction of Student model based on BP neural network

Liu

Zhang

2019

2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

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With the development of personalized learning, the construction of student models is becoming more and more important. At present, there are still problems in the student model that the characteristics are single and the indicators of each dimension are not clear. In this paper, learners will be analyzed from the perspective of student characteristics. And BP (Back Propagation) neural network algorithm will be used to establish a personalized student model. This paper first constructs the feature system of the student model from six dimensions. Secondly, the initial data is obtained through questionnaire survey, and the data is initialized to obtain 30 feature vectors as input to BP neural network. The output of the network is a learner type, which is divided into 36 categories. The construction of the student model will have certain practical significance for realizing the effectiveness of personalized education in distance education.

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

Cited by 18 publications

References 26 publications

Technology-enhanced assessment visualization for smart learning environments

Technology-enhanced assessment visualization for smart learning environments

Uma Abordagem Baseada em Ontologias para Modelagem e Avaliação do Estudante em Sistemas Adaptativos e Inteligentes para Educação

Construction of Student model based on BP neural network

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