Sequencing in Web-Based Education: Approaches, Standards and Future Trends

Gutiérrez, Sergio; Pardo, B.

doi:10.1007/978-3-540-71974-8_5

Cited by 8 publications

(1 citation statement)

References 18 publications

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“…Koper (2005) purposes one system that suggests the next activities based on successfully completed (by other students) activities. Gutiérrez and Pardo (2007) suggest the use of annotations, indicating how many students have already done and how many have successfully completed a given activity.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Recommender Strategy in Learning: an Experimental Investigation

Madeira¹,

Abreu²,

Quaresma³

2013

Social Technologies

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Purpose-finding ways of improving learning in a formal higher education context. Design/methodology/approach-in the proposed model we will consider extending traditional content management systems, giving learners the possibility to add new materials and to rate them, and a hybrid strategy that combines technical recommendations with some profile-based filtering to offer adaptive and suitable sequencing learning content to learners. Findings-the experiment shows that our recommendation techniques are able to reflect the learners' interests. Research limitations/implications-it's necessary to demonstrate the contributions of these kinds of solutions to the learners' success. Practical implications-theoretical and practical framework for future research in the field was developed.

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Section: Related Workmentioning

confidence: 99%

Hybrid Recommender Strategy in Learning: an Experimental Investigation

Madeira¹,

Abreu²,

Quaresma³

2013

Social Technologies

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show abstract

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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Evolutionary computation approaches to the Curriculum Sequencing problem

AlMuhaideb

Menaï

2011

Nat Comput

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Sequencing in Web-Based Education: Approaches, Standards and Future Trends

Cited by 8 publications

References 18 publications

Hybrid Recommender Strategy in Learning: an Experimental Investigation

Hybrid Recommender Strategy in Learning: an Experimental Investigation

A comparative analysis of metaheuristics applied to adaptive curriculum sequencing

Evolutionary computation approaches to the Curriculum Sequencing problem

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