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• W2886763945 abstract "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." @default.
• W2886763945 created "2018-08-22" @default.
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• W2886763945 date "2018-08-08" @default.
• W2886763945 modified "2023-10-18" @default.
• W2886763945 title "Swarm intelligence to solve the curriculum sequencing problem" @default.
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• W2886763945 doi "https://doi.org/10.1002/cae.22046" @default.
• W2886763945 hasPublicationYear "2018" @default.
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