Swarm intelligence to solve the curriculum sequencing problem

Menaï, Mohamed El Bachir; Alhunitah, Hamad; Al-Salman, Hussein

doi:10.1002/cae.22046

Cited by 17 publications

(4 citation statements)

References 48 publications

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“…Resource sequencing in e-learning environments is one of the outer segments that have the opportunity to positively influence the interests and engagement of learners, as well as to make the whole learning process as effective as possible [192]. Standard personalisation procedures for e-learning environments focus primarily on designing and delivering resources tailored to users' preferences.…”

Section: Discussionmentioning

confidence: 99%

“…It is one of the main techniques for constructing a learning path for each student that can be found in many early articles [5,10,77,135]. The specific form of this algorithm is the memetic algorithm, which applies a separate local search process to refine individuals [95,192,246]. Other common algorithms used for resource sequencing are Ant Colony Optimisation [207,229,241], Particle Swarm Optimisation [16,172] and Artificial Bee Colony [134,269].…”

Section: Resource Sequencingmentioning

confidence: 99%

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Personalisation methods in e‐learning‐A literature review

Mikić¹,

Ilić²,

Kopanja³

et al. 2022

Comp Applic In Engineering

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Over the years, personalisation in e‐learning has evolved as a promising paradigm for matching learners' specific requirements. Although many literature reviews on personalised e‐learning have been conducted, there are still voids and limitations in the comprehensive literature survey regarding how personalisation might occur in e‐learning environments and benefit the teaching and learning process. In addition, no design guidelines, frameworks, or tools to guide its implementation exist, so practice can vary widely. The principal purpose of this literature review is to examine the subject, provide insights into the use of personalised techniques and systems in e‐learning, and investigate its impact on the engagement and success of the students. This study applies methods of precise selection criteria to determine which of the selected publications have the strongest interconnection and relevance with the topic of e‐learning personalisation. It also categorises the personalisation techniques employed by each identified system and highlights new perspectives and advances in the area.

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Section: Discussionmentioning

confidence: 99%

Section: Resource Sequencingmentioning

confidence: 99%

Personalisation methods in e‐learning‐A literature review

Mikić¹,

Ilić²,

Kopanja³

et al. 2022

Comp Applic In Engineering

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“…In (Menai et al. 2018 ), the performance of the solutions found was evaluated in a real e-learning environment on real data from an information technology diploma program and 2,000 learners selected randomly at Buraydah College of Technology 1 . The data were gathered from the Learner Affairs System.…”

Section: Related Workmentioning

confidence: 99%

“… 2012 ), PSO vs GA vs ACO vs Immune Algorithm (Wan and Niu 2016 ) and different implementations of PSO and/or ACO (Menai et al. 2018 ; Chandar et al. 2010 ).…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of metaheuristics applied to adaptive curriculum sequencing

et al. 2021

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The effective adoption of online learning depends on user satisfaction as distance education approaches suffer from a lack of commitment that may lead to failures and dropouts. The adaptive learning literature argues that an alternative to achieve student satisfaction is to treat them individually, delivering the educational content in a personalized manner. In addition, the sequencing of this content—called Adaptive Curriculum Sequencing (ACS)—is important to avoid cognitive overload and disorientation. The search for an optimal sequence from ever-growing databases is an NP-Hard combinatorial optimization problem. Although some approaches have been proposed, it is challenging to assess their contributions due to the lack of benchmark data available. This paper presents a procedure to create synthetic dataset to evaluate ACS approaches and, as a concept proof, analyzes metaheuristics usually used in ACS approaches: Genetic Algorithm, Particle Swarm Optimization (PSO) and Prey–Predator Algorithm using student’s learning goals and their extrinsic and intrinsic information. We also propose an approach based on Differential Evolution (DE). The computational experiments include synthetic datasets with a varied amount of learning materials and real-world datasets for comparison. The results show that DE performed better than the other methods when less than 500 learning materials are used while PSO performed better for larger problems.

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