Enhancing Performance of Lazy Learner by Means of Binary Particle Swarm Optimization

Punjabi, Mamta; Prajapati, Gend Lal

doi:10.1109/tribes52498.2021.9751675

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…Four algorithms are selected for comparative study. They are BPSO [6,39], PSO [40,41], DE [29,42], and LSHADE-cnEpSin [11]. The reasons for selecting these algorithms are: ( 1 All the algorithms are executed on 100 learners, respectively.…”

Section: Discussionmentioning

confidence: 99%

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Learning Path Optimization based on Multi-Attribute Matching and Variable Length Continuous Representation

Zhang

Xiao

Song

et al. 2022

Preprint

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Personalized learning path considers both learner and resource attributes. The evolutionary algorithm approach usually forms the learning path generation problem into a problem that optimizes the matching degree of the learner and the generated learning path. The proposed work considers the learner attributes as ability level, learning objective, learning style, and expected learning time. The learning path attributes include difficulty level, covered concept, supported learning styles, required learning time, and prerequisites. A variable-length representation of the learning path is adopted based on floating numbers, which significantly reduces the encoding length and simplifies the learning path generating process. An improved differential evolution algorithm is applied to optimize the matching degree of learning path and learner. The quantitative experiments on different problem scales show the proposed system outperforms the binary-based representation approaches in scaling ability and outperforms the comparative algorithms in efficiency.

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Section: Discussionmentioning

confidence: 99%

“…Population size is set according to [39] 𝑐𝑐1 = 𝑐𝑐2 = 2 Learning factors are set according to [39] Parameters regarding DE…”

Section: 𝑁𝑁𝑀𝑀 = 50mentioning

confidence: 99%

Learning Path Optimization based on Multi-Attribute Matching and Variable Length Continuous Representation

Zhang

Xiao

Song

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Four algorithms are selected for comparative study. They are BPSO [9,46], PSO [47,48], DE [31,37], and LSHADE-cnEpSin [14]. The reasons for selecting these algorithms are: (1) BPSO is the binary version of the original Particle Swarm Optimization algorithm, which has proved effective over the Genetic Algorithm for e-course composition problems [9]; (2) PSO and DE are well-known optimizers for the continuous problem; (3) DE is the basic version of all the modified algorithms related to LSHADE-cnEpSin; (4) LSHADE-cnEpSin is the second winner in the competition of CEC 2017.…”

Section: Discussionmentioning

confidence: 99%

Learning Path Optimization Based on Multi-Attribute Matching and Variable Length Continuous Representation

et al. 2022

View full text Add to dashboard Cite

Personalized learning path considers matching symmetrical attributes from both learner and learning material. The evolutionary algorithm approach usually forms the learning path generation problem into a problem that optimizes the matching degree of the learner and the generated learning path. The proposed work considers the matching of the following symmetrical attributes of learner/material: ability level/difficulty level, learning objective/covered concept, learning style/supported learning styles, and expected learning time/required learning time. The prerequisites of material are considered constraints. A variable-length representation of the learning path is adopted based on floating numbers, which significantly reduces the encoding length and simplifies the learning path generating process. An improved differential evolution algorithm is applied to optimize the matching degree of learning path and learner. The quantitative experiments on different problem scales show that the proposed system outperforms the binary-based representation approaches in scaling ability and outperforms the comparative algorithms in efficiency.

show abstract

Enhancing Performance of Lazy Learner by Means of Binary Particle Swarm Optimization

Cited by 2 publications

References 14 publications

Learning Path Optimization based on Multi-Attribute Matching and Variable Length Continuous Representation

Learning Path Optimization based on Multi-Attribute Matching and Variable Length Continuous Representation

Learning Path Optimization Based on Multi-Attribute Matching and Variable Length Continuous Representation

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