$ PC^{2} $PSO: personalized e-course composition based on Particle Swarm Optimization
Abstract This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called $ PC^{2} $PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The $ PC^{2} $PSO composes a personalized e-course ac...
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| Autor*in: |
Chu, Chih-Ping [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2009 |
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| Schlagwörter: |
Intelligent Tutoring System (ITS) Particle Swarm Optimization (PSO) |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2009 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 34(2009), 1 vom: 16. Juni, Seite 141-154 |
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| Übergeordnetes Werk: |
volume:34 ; year:2009 ; number:1 ; day:16 ; month:06 ; pages:141-154 |
| Links: |
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| DOI / URN: |
10.1007/s10489-009-0186-7 |
|---|
| Katalog-ID: |
OLC2066095958 |
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| 520 | |a Abstract This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called $ PC^{2} $PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The $ PC^{2} $PSO composes a personalized e-course according to (1) whether or not the covered learning concepts of the personalized e-course meets the expected learning target of a learner, (2) whether or not the difficulty of the e-learning material matches a learner’s ability, (3) the limitation of learning time for individual learners, and (4) the balance of the weight of learning concepts that are covered in a personalized e-course. $ PC^{2} $PSO can provide a truly personalized learning environment when used in conjunction with an Intelligent Tutoring System (ITS). When an e-course authoring tool is based on the proposed approach, the $ PC^{2} $PSO can facilitate instructors in selecting appropriate e-learning materials from a mass of candidate e-learning materials, and then saves time and effort in the e-course editing process. | ||
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| 650 | 4 | |a Intelligent Tutoring System (ITS) | |
| 650 | 4 | |a Particle Swarm Optimization (PSO) | |
| 650 | 4 | |a Personalized e-course composition | |
| 650 | 4 | |a Personalized learning | |
| 700 | 1 | |a Chang, Yi-Chun |4 aut | |
| 700 | 1 | |a Tsai, Cheng-Chang |4 aut | |
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10.1007/s10489-009-0186-7 doi (DE-627)OLC2066095958 (DE-He213)s10489-009-0186-7-p DE-627 ger DE-627 rakwb eng 004 VZ Chu, Chih-Ping verfasserin aut $ PC^{2} $PSO: personalized e-course composition based on Particle Swarm Optimization 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2009 Abstract This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called $ PC^{2} $PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The $ PC^{2} $PSO composes a personalized e-course according to (1) whether or not the covered learning concepts of the personalized e-course meets the expected learning target of a learner, (2) whether or not the difficulty of the e-learning material matches a learner’s ability, (3) the limitation of learning time for individual learners, and (4) the balance of the weight of learning concepts that are covered in a personalized e-course. $ PC^{2} $PSO can provide a truly personalized learning environment when used in conjunction with an Intelligent Tutoring System (ITS). When an e-course authoring tool is based on the proposed approach, the $ PC^{2} $PSO can facilitate instructors in selecting appropriate e-learning materials from a mass of candidate e-learning materials, and then saves time and effort in the e-course editing process. E-learning Intelligent Tutoring System (ITS) Particle Swarm Optimization (PSO) Personalized e-course composition Personalized learning Chang, Yi-Chun aut Tsai, Cheng-Chang aut Enthalten in Applied intelligence Springer US, 1991 34(2009), 1 vom: 16. Juni, Seite 141-154 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:34 year:2009 number:1 day:16 month:06 pages:141-154 https://doi.org/10.1007/s10489-009-0186-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_130 GBV_ILN_2020 AR 34 2009 1 16 06 141-154 |
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10.1007/s10489-009-0186-7 doi (DE-627)OLC2066095958 (DE-He213)s10489-009-0186-7-p DE-627 ger DE-627 rakwb eng 004 VZ Chu, Chih-Ping verfasserin aut $ PC^{2} $PSO: personalized e-course composition based on Particle Swarm Optimization 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2009 Abstract This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called $ PC^{2} $PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The $ PC^{2} $PSO composes a personalized e-course according to (1) whether or not the covered learning concepts of the personalized e-course meets the expected learning target of a learner, (2) whether or not the difficulty of the e-learning material matches a learner’s ability, (3) the limitation of learning time for individual learners, and (4) the balance of the weight of learning concepts that are covered in a personalized e-course. $ PC^{2} $PSO can provide a truly personalized learning environment when used in conjunction with an Intelligent Tutoring System (ITS). When an e-course authoring tool is based on the proposed approach, the $ PC^{2} $PSO can facilitate instructors in selecting appropriate e-learning materials from a mass of candidate e-learning materials, and then saves time and effort in the e-course editing process. E-learning Intelligent Tutoring System (ITS) Particle Swarm Optimization (PSO) Personalized e-course composition Personalized learning Chang, Yi-Chun aut Tsai, Cheng-Chang aut Enthalten in Applied intelligence Springer US, 1991 34(2009), 1 vom: 16. Juni, Seite 141-154 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:34 year:2009 number:1 day:16 month:06 pages:141-154 https://doi.org/10.1007/s10489-009-0186-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_130 GBV_ILN_2020 AR 34 2009 1 16 06 141-154 |
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10.1007/s10489-009-0186-7 doi (DE-627)OLC2066095958 (DE-He213)s10489-009-0186-7-p DE-627 ger DE-627 rakwb eng 004 VZ Chu, Chih-Ping verfasserin aut $ PC^{2} $PSO: personalized e-course composition based on Particle Swarm Optimization 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2009 Abstract This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called $ PC^{2} $PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The $ PC^{2} $PSO composes a personalized e-course according to (1) whether or not the covered learning concepts of the personalized e-course meets the expected learning target of a learner, (2) whether or not the difficulty of the e-learning material matches a learner’s ability, (3) the limitation of learning time for individual learners, and (4) the balance of the weight of learning concepts that are covered in a personalized e-course. $ PC^{2} $PSO can provide a truly personalized learning environment when used in conjunction with an Intelligent Tutoring System (ITS). When an e-course authoring tool is based on the proposed approach, the $ PC^{2} $PSO can facilitate instructors in selecting appropriate e-learning materials from a mass of candidate e-learning materials, and then saves time and effort in the e-course editing process. E-learning Intelligent Tutoring System (ITS) Particle Swarm Optimization (PSO) Personalized e-course composition Personalized learning Chang, Yi-Chun aut Tsai, Cheng-Chang aut Enthalten in Applied intelligence Springer US, 1991 34(2009), 1 vom: 16. Juni, Seite 141-154 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:34 year:2009 number:1 day:16 month:06 pages:141-154 https://doi.org/10.1007/s10489-009-0186-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_130 GBV_ILN_2020 AR 34 2009 1 16 06 141-154 |
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10.1007/s10489-009-0186-7 doi (DE-627)OLC2066095958 (DE-He213)s10489-009-0186-7-p DE-627 ger DE-627 rakwb eng 004 VZ Chu, Chih-Ping verfasserin aut $ PC^{2} $PSO: personalized e-course composition based on Particle Swarm Optimization 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2009 Abstract This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called $ PC^{2} $PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The $ PC^{2} $PSO composes a personalized e-course according to (1) whether or not the covered learning concepts of the personalized e-course meets the expected learning target of a learner, (2) whether or not the difficulty of the e-learning material matches a learner’s ability, (3) the limitation of learning time for individual learners, and (4) the balance of the weight of learning concepts that are covered in a personalized e-course. $ PC^{2} $PSO can provide a truly personalized learning environment when used in conjunction with an Intelligent Tutoring System (ITS). When an e-course authoring tool is based on the proposed approach, the $ PC^{2} $PSO can facilitate instructors in selecting appropriate e-learning materials from a mass of candidate e-learning materials, and then saves time and effort in the e-course editing process. E-learning Intelligent Tutoring System (ITS) Particle Swarm Optimization (PSO) Personalized e-course composition Personalized learning Chang, Yi-Chun aut Tsai, Cheng-Chang aut Enthalten in Applied intelligence Springer US, 1991 34(2009), 1 vom: 16. Juni, Seite 141-154 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:34 year:2009 number:1 day:16 month:06 pages:141-154 https://doi.org/10.1007/s10489-009-0186-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_130 GBV_ILN_2020 AR 34 2009 1 16 06 141-154 |
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$ PC^{2} $PSO: personalized e-course composition based on Particle Swarm Optimization |
| abstract |
Abstract This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called $ PC^{2} $PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The $ PC^{2} $PSO composes a personalized e-course according to (1) whether or not the covered learning concepts of the personalized e-course meets the expected learning target of a learner, (2) whether or not the difficulty of the e-learning material matches a learner’s ability, (3) the limitation of learning time for individual learners, and (4) the balance of the weight of learning concepts that are covered in a personalized e-course. $ PC^{2} $PSO can provide a truly personalized learning environment when used in conjunction with an Intelligent Tutoring System (ITS). When an e-course authoring tool is based on the proposed approach, the $ PC^{2} $PSO can facilitate instructors in selecting appropriate e-learning materials from a mass of candidate e-learning materials, and then saves time and effort in the e-course editing process. © Springer Science+Business Media, LLC 2009 |
| abstractGer |
Abstract This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called $ PC^{2} $PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The $ PC^{2} $PSO composes a personalized e-course according to (1) whether or not the covered learning concepts of the personalized e-course meets the expected learning target of a learner, (2) whether or not the difficulty of the e-learning material matches a learner’s ability, (3) the limitation of learning time for individual learners, and (4) the balance of the weight of learning concepts that are covered in a personalized e-course. $ PC^{2} $PSO can provide a truly personalized learning environment when used in conjunction with an Intelligent Tutoring System (ITS). When an e-course authoring tool is based on the proposed approach, the $ PC^{2} $PSO can facilitate instructors in selecting appropriate e-learning materials from a mass of candidate e-learning materials, and then saves time and effort in the e-course editing process. © Springer Science+Business Media, LLC 2009 |
| abstract_unstemmed |
Abstract This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called $ PC^{2} $PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The $ PC^{2} $PSO composes a personalized e-course according to (1) whether or not the covered learning concepts of the personalized e-course meets the expected learning target of a learner, (2) whether or not the difficulty of the e-learning material matches a learner’s ability, (3) the limitation of learning time for individual learners, and (4) the balance of the weight of learning concepts that are covered in a personalized e-course. $ PC^{2} $PSO can provide a truly personalized learning environment when used in conjunction with an Intelligent Tutoring System (ITS). When an e-course authoring tool is based on the proposed approach, the $ PC^{2} $PSO can facilitate instructors in selecting appropriate e-learning materials from a mass of candidate e-learning materials, and then saves time and effort in the e-course editing process. © Springer Science+Business Media, LLC 2009 |
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| title_short |
$ PC^{2} $PSO: personalized e-course composition based on Particle Swarm Optimization |
| url |
https://doi.org/10.1007/s10489-009-0186-7 |
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| author2 |
Chang, Yi-Chun Tsai, Cheng-Chang |
| author2Str |
Chang, Yi-Chun Tsai, Cheng-Chang |
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| doi_str |
10.1007/s10489-009-0186-7 |
| up_date |
2024-07-04T03:45:17.068Z |
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