Evolutionary computation approaches to the Curriculum Sequencing problem

AlMuhaideb, Sarab; Menaï, Mohamed El Bachir

doi:10.1007/s11047-010-9246-5

Cited by 45 publications

(36 citation statements)

References 42 publications

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“…Devido a limitação de espaço, as abordagens de CE não serão detalhadas neste trabalho. Todavia, maiores informações sobre as meta-heurísticas (OCF, OEP e Algoritmo Genético -AG) utilizadas no problema da SCA podem ser encontradas em [Al-Muhaideb and Menai 2011]. Informações sobre o Algoritmo de Busca Harmônica (ABH) podem ser encontradas em [Hnida et al 2016].…”

Section: Modelo De Comparaçãounclassified

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Geração de Sequências Curriculares Adaptativas baseada em Computação Evolucionária: Estado da Arte e Tendências

Barrére¹,

Souza²,

Machado³

2017

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

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Abstract. For effective adoption of e-Learning systems, the student's needs and context must be taken into consideration when delivering content. One of the most interesting challenges in this area is the selection of an Adaptive Curriculum Sequencing that provides individualized instruction. Such a challenge is known in the literature as an NP-hard problem, causing frequent use of metaheuristics, particularly of Evolutionary Computing, to approximate the best solution. In this paper a review of the lastest works in this field of research is presented with a comparison model according to the variables used for the sequencing.Resumo. Para efetiva adoção dos sistemas de e-Learning, as necessidades e o contexto do aluno precisam ser levados em consideração na entrega de conteúdo. Assim, um dos desafios mais interessantes daáreaé a seleção de uma Sequência Curricular Adaptativa que forneça um ensino individualizado. Tal desafioé conhecido na literatura como um problema NP-Difícil, causando uma frequente utilização de meta-heurísticas, particularmente de Computação Evolucionária, para aproximarà melhor solução. Neste artigoé feita uma revisão dosúltimos trabalhos neste campo de pesquisa, apresentando um modelo de comparação de acordo com as variáveis utilizadas para o sequenciamento. IntroduçãoA efetiva utilização de sistemas de e-Learning depende da criação de soluções para permanência dos alunos nos cursos ofertados [Davis et al. 2016] e de uma correta administração dos materiais didáticos para serem disponibilizados de acordo com a necessidade de aprendizagem de cada aluno [Van Merriënboer and Ayres 2005]. Sistemas de e-Learning adaptativos buscam resolver esses problemas levando em consideração as informações de cada aluno, tais como suas necessidades e contextos, podendo assim, recomendar conteúdo didático de maneira individualizada [Pushpa 2012]. [Al-Azawei and Badii 2014] pontuam alguns dos objetivos de um e-Learning adaptativo: melhorar a assimilação de conteúdo, reduzir o esquecimento, motivar os alunos, orientar o melhor caminho de aprendizagem, tratar a sobrecarga cognitiva, reduzir o custo de aprendizagem e melhorar a usabilidade dos sistemas. Isso significa que a adaptatividade pode desempenhar o papel de promover e melhorar a aprendizagem.[ Premlatha and Geetha 2015] indicam que existem diferentes níveis de adaptação, dentre eles o baseado na sequência curricular. A ideia desse nívelé auxiliar o sistema a

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Section: Modelo De Comparaçãounclassified

“…As meta-heurísticas OCF e AG continuam sendo as abordagens de CE mais utilizadas para o problema da SCA ([Al-Muhaideb and Menai 2011]). É possível perceber uma relação com o tipo de sequenciamento, onde, na maioria dos trabalhos, OCF está relacionada ao SS, assim como AG está relacionado ao SI.…”

Section: Artigounclassified

Geração de Sequências Curriculares Adaptativas baseada em Computação Evolucionária: Estado da Arte e Tendências

Barrére¹,

Souza²,

Machado³

2017

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

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“…Many works in the literature proposed evolutionary algorithm, such as Genetic Algorithm [8,11], Particle Swarm Optimization [10,15], or Ant Colony Optimization [25,27], to solve learning path adaptation based on Constraint Satisfaction Problem [28]. Among this method, ant colony optimization is the most used to solve the learning path problem [28].…”

Section: Approaches To Learning Path Problemmentioning

confidence: 99%

“…In the perspective of learning path, individual learner is typically modeled as an ant, while the sequence of course is modeled as arcs between nodes [28]. Furthermore, ants or learners try to find the shortest path from their nest to the food source, which in this case is pedagogical objective, in a reasonable time.…”

Section: Approaches To Learning Path Problemmentioning

confidence: 99%

“…Some researcher [23] suggested to given consideration to the real-world learning objects and the number of students who visit the same learning objects. In learning path adaptation, this approached is called as social sequencing [28]. As each learning object represents different concepts to be learned, it is difficult for the students to determine the learning path for visiting the target objects without any guidance.…”

Section: A Implementationmentioning

confidence: 99%

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Learning path adaptation in online learning systems

Muhammad

Zhou

Beydoun

et al. 2016

2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

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Learning path in online learning systems refers to a sequence of learning objects which are designated to help the students in improving their knowledge or skill in particular subjects or degree courses. In this paper, we review the recent research on learning path adaptation to pursue two goals, first is to organize and analyze the parameter of adaptation in learning path; the second is to discuss the challenges in implementing learning path adaptation. The survey covers the state of the art and aims at providing a comprehensive introduction to the learning path adaptation for researchers and practitioners. Abstract-Learning path in online learning systems refers to a sequence of learning objects which are designated to help the students in improving their knowledge or skill in particular subjects or degree courses. In this paper, we review the recent research on learning path adaptation to pursue two goals, first is to organize and analyze the parameter of adaptation in learning path; the second is to discuss the challenges in implementing learning path adaptation. The survey covers the state of the art and aims at providing a comprehensive introduction to the learning path adaptation for researchers and practitioners.

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Swarm intelligence to solve the curriculum sequencing problem

Menaï

Alhunitah

Al-Salman

2018

Comp Applic In Engineering

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The curriculum sequencing (CS) problem asks to find a personalized optimal path for a learner in an e‐learning environment. Solving this problem contributes to developing adaptive e‐learning systems which provide personalized learning paths compatible with the individual profiles of the learners. The solutions to large CS instances can be only approximated as this problem is NP‐hard. In this paper, we formulate the CS problem as a constraint satisfaction problem and investigate swarm intelligence methods to solve it, including a newly introduced method called SwarmRW and the widely used ant colony system (ACS). In addition, we introduce two variants of SwarmRW, called SwarmRW_rnd and SwarmRW_inc. The performance results obtained on real data show that SwarmRW_rnd achieved the best results in terms of maximizing the number of satisfied constraints. However, SwarmRW_inc demonstrated the best trade‐off between the solution quality and the running‐time to obtain it.

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Evolutionary computation approaches to the Curriculum Sequencing problem

Cited by 45 publications

References 42 publications

Geração de Sequências Curriculares Adaptativas baseada em Computação Evolucionária: Estado da Arte e Tendências

Geração de Sequências Curriculares Adaptativas baseada em Computação Evolucionária: Estado da Arte e Tendências

Learning path adaptation in online learning systems

Swarm intelligence to solve the curriculum sequencing problem

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