Collaborative optimization algorithm for learning path construction in E-learning

Vanitha, V.; Krishnan, Prajindra Sankar; Elakkiya, R.

doi:10.1016/j.compeleceng.2019.06.016

Cited by 57 publications

(31 citation statements)

References 14 publications

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“…Several studies have also discussed E-Learning as a common methodology that is becoming user-friendly as it provides relaxed access and usage [6,7]. Furthermore, E-Learning allows students to study and learn freely using cuttingedge new technology without conventional teaching approaches like direct supervision and control mechanisms [8][9][10]. The fast development in IT with new software and high configuration of hardware have made E-Learning more easy and useful, which then improves university learning outcomes [11].…”

Section: Introductionmentioning

confidence: 99%

Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

et al. 2020

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Learning using the Internet or training through E-Learning is growing rapidly and is increasingly favored over the traditional methods of learning and teaching. This radical shift is directly linked to the revolution in digital computer technology. The revolution propelled by innovation in computer technology has widened the scope of E-Learning and teaching, whereby the process of exchanging information has been made simple, transparent, and effective. The E-Learning system depends on different success factors from diverse points of view such as system, support from the institution, instructor, and student. Thus, the effect of critical success factors (CSFs) on the E-Learning system must be critically analyzed to make it more effective and successful. This current paper employed the analytic hierarchy process (AHP) with group decision-making (GDM) and Fuzzy AHP (FAHP) to study the diversified factors from different dimensions of the web-based E-Learning system. The present paper quantified the CSFs along with its dimensions. Five different dimensions and 25 factors associated with the web-based E-Learning system were revealed through the literature review and were analyzed further. Furthermore, the influence of each factor was derived successfully. Knowing the impact of each E-Learning factor will help stakeholders to construct education policies, manage the E-Learning system, perform asset management, and keep pace with global changes in knowledge acquisition and management.

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Section: Introductionmentioning

confidence: 99%

Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

et al. 2020

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“…Dentre as abordagens bioinspiradas, o Algoritmo Otimização de Colônia de Formigas foi explorado em (Rastegarmoghadam & Ziarati, 2017) e Costa, N. T., Fernandes, M. A. RBIE v.29 -2021 488 (Vanitha, Krishnan, & Elakkiya, 2019). Já o Algoritmo Genético é explorado em (Lin, Chang, & Chu, 2016), (Hssina, 2019), (de Miranda, et al, 2019) e (Vanitha, Krishnan, & Elakkiya, 2019). Além dessas técnicas, o Algoritmo Presa-Predador é abordado em (Machado, Barrére, & Souza, 2018).…”

Section: Planejamento Automatizado Apoiado Por Computação Bioinspiradaunclassified

Sequenciamento de Ações Pedagógicas baseadas na Taxonomia de Bloom usando Planejamento Automatizado apoiado por Algoritmo Genético

Costa

Fernandes

2021

RBIE

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Este trabalho investigou o planejamento em Inteligência Artificial para o sequenciamento de ações pedagógicas, de acordo com o perfil do estudante. As ações representaram o processo cognitivo dado pela Taxonomia de Bloom (TB) e o perfil do estudante foi determinado conforme o Revised Approaches to Studying Inventory (RASI). A fim de medir a adequação de uma sequência ao perfil do estudante, foi necessário mapear estas duas teorias, sendo este mapeamento uma das contribuições deste estudo. Assim, o sequenciamento de ações foi formulado como um problema de otimização e implementado através de um algoritmo genético. A proposição da função a ser otimizada para este problema é também uma contribuição, desde que estabelecer critérios para avaliar aspectos pedagógicos tem sido um desafio para Informática na Educação. Experimentos realizados contaram com 41 participantes que responderam ao questionário RASI e, após terem recebido e analisado as respectivas sequências de ações determinadas pelo planejador proposto neste trabalho, também responderam a um questionário de satisfação em relação à sequência. Os resultados obtidos se mostraram promissores, demonstrando a viabilidade da pesquisa.

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“…The algorithm adaptively and dynamically finds the optimal path which is more suitable for learners in terms of learning preferences, knowledge levels and historical data on learning, and can improve learner performance and satisfaction. V. Vanitha et al [27] combined ant colony optimization and genetic algorithms to propose a collaborative optimization learning path planning algorithm. The algorithm matches the learner characteristics with the learning content sequence, and gives personalized learning paths to online learners, thereby improving their online learning capabilities.…”

Section: Related Workmentioning

confidence: 99%

“…According to formula (27), calculate the Cosine similarity of w i vector of each row in the eigenvector matrix V (e) h * m and that of w j vector of each row in the eigenvector matrix…”

Section: E Learning Behavior Similaritymentioning

confidence: 99%

On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior

Shou

et al. 2020

IEEE Access

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Studying learning path planning can help find useful implicit learning behavior patterns from learners' online learning behavior data, which is conducive to helping beginners or learners with low participation to reasonably arrange the learning sequence of online knowledge points. This paper proposes a learning path planning algorithm based on collaborative analysis of learning behavior through collaborative data analysis of online learning behaviors. The algorithm, based on the learner's online learning behavior data set, first establishes the concept interaction degree model of knowledge points and the directed learning path network, and proposes a local structure similarity measurement method between the knowledge nodes of the directed learning path network. Second, based on the learner's Kullback-Leibler divergence (KLD) matrix, a learning behavior similarity calculation method on the basis of eigenvector matrix similarity is proposed, which is used to perform cluster analysis on learners with similar learning behaviors and to analyze the personalized optimal learning path of each kind of learners. Finally, the clustering algorithm and the evaluation index of the directed complex network have both verified the advantages of the algorithm. This paper employs the online behavior data set and online test data set obtained from the e-learning platform to conduct an empirical analysis of the learning path planning algorithm proposed hereof. The results show that the learner's learning effect has been improved, verifying the validity and reliability of the algorithm. INDEX TERMS local structural similarity on the knowledge node of the directed learning path network, similarity measure on learning behavior, concept interaction degree of knowledge points, individualized optimal learning path

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Collaborative optimization algorithm for learning path construction in E-learning

Cited by 57 publications

References 14 publications

Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

Sequenciamento de Ações Pedagógicas baseadas na Taxonomia de Bloom usando Planejamento Automatizado apoiado por Algoritmo Genético

On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior

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