Skip to main content
SpringerLink
Log in

PC2PSO: personalized e-course composition based on Particle Swarm Optimization

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called PC2PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The PC2PSO 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. PC2PSO 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 PC2PSO 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen CM, Lee HM, Chen YH (2005) Personalized e-learning system using Item Response Theory. Comput Educ 44(3):237–255

    Article  Google Scholar 

  2. Shareable Content Object Reference Model (SCORM) Version 1.3, Advanced Distributed Learning (ADL) (2004). [Online]. Available: http://www.adlnet.org/

  3. Gomez-Albarran M (2005) The teaching and learning of programming: a survey of supporting software tools. Comput J 48(2):130–144

    Article  Google Scholar 

  4. IMS (2004) Learning Resource Meta-data Information Model Version 1.2.2, IMS Global Learning Consortium. [Online]. Available: http://www.imsglobal.org/

  5. Reload (2004) Reload Tool. [Online]. Available: http://www.reload.ac.uk

  6. Yang JTD, Chiu CH, Tsai CY, Wu TH (2004) Visualized online simple sequencing authoring tool for SCORM-compliant content package. In: Proceedings of IEEE international conference on advanced learning technologies, pp 609–613

  7. Su JM, Tseng SS, Chen CY, Weng JF, Tsai WN (2006) Constructing SCORM compliant course based on High-Level Petri Nets. Comput Stand Interfaces 28(3):336–355

    Article  Google Scholar 

  8. Brusilovsky P (1999) Adaptive and intelligent technologies for Web-based education. Kunstl Intell 13:19–25

    Google Scholar 

  9. Lee MG (2001) Profiling students’ adaptation styles in Web-based learning. Comput Educ 36(2):121–132

    Article  Google Scholar 

  10. Liu HI, Yang MN (2005) QoL guaranteed adaptation and personalization in E-learning systems. IEEE Trans Educ 48(4):676–687

    Article  Google Scholar 

  11. Iglesias A, Martínez, P, Aler, R, Fernández, F (2008) Learning teaching strategies in an adaptive and intelligent educational system through reinforcement learning. Appl Intell. doi:10.1007/s10489-008-0115-1

    Google Scholar 

  12. Brusilovsky P, Eklund J, Schwarz E (1998) Web-based education for all: a tool for development adaptive courseware computer networks and ISDN systems, vol 30, pp 291–300

  13. Brusilovsky P, Maybury MT (2002) From adaptive hypermedia to the adaptive web. Commun ACM 45(5):30–33

    Article  Google Scholar 

  14. Huang MJ, Huang HS, Chen MY (2007) Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Syst Appl 33(3):551–564

    Article  Google Scholar 

  15. Luo J, Kong W, Ge L (2008) Implementation of learning path in process control model. Comput J. doi:10.1093/comjnl/bxn1012

    Google Scholar 

  16. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948

  17. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: Proceedings of IEEE international conference on systems, man, and cybernetics, vol 5, pp 4104–4108

  18. Goldberg DE (1989) Genetic algorithms in search, optimization & machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  19. Mitchell M (1996) An introduction to genetic algorithms. Bradford Books. MIT Press, Cambridge

    Google Scholar 

  20. Rothlauf F (2006) Representations for genetic and evolutionary algorithms. Springer, New York

    Google Scholar 

  21. Hong TP, Wang HS, Lin WY, Lee WY (2002) Evolution of appropriate crossover and mutation operators in a genetic process. Appl Intell 16(1):7–17

    Article  MATH  Google Scholar 

  22. Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423–1447

    Article  Google Scholar 

  23. Yoshida H, Kawata K, Fukuyama Y, Takayama S, Nakanishi Y (2000) A particle swarm optimization for reactive power and voltagecontrol considering voltage security assessment. IEEE Trans Power Syst 15(4):1232–1239

    Article  Google Scholar 

  24. Naka S, Genji T, Yura T, Fukuyama Y (2003) A hybrid particle swarm optimization for distribution state estimation. IEEE Trans Power Syst 18(1):60–68

    Article  Google Scholar 

  25. Yin PY, Yu SS, Wang PP, Wang YT (2006) A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems. Comput Stand Interfaces 28(4):441–450

    Article  Google Scholar 

  26. Lim A, Lin J, Xiao F (2007) Particle Swarm Optimization and Hill Climbing for the bandwidth minimization problem. Appl Intell 26:175–182

    Article  MATH  Google Scholar 

  27. Yin PY, Chang KC, Hwang GJ, Hwang GH, Chan Y (2006) A particle swarm optimization approach to composing serial test sheets for multiple assessment criteria. Educ Technol Soc 9(3):3–15

    Google Scholar 

  28. Huang TC, Huang YM, Cheng SC (2007) Automatic and interactive e-learning auxiliary material generation utilizing particle swarm optimization. Expert Syst Appl 35(4):2113–2122

    Article  Google Scholar 

  29. De-Marcos L, Pages C, Martinez JJ, Gutierrez JA (2007) Competency-based learning object sequencing using particle swarms. In: Proceedings of 19th IEEE international conference on tools with artificial intelligence 2007 (ICTAI 2007), vol 2, pp 111–116

  30. Cheng SC, Lin YT, Huang YM (2009) Dynamic question generation system for web-based testing using particle swarm optimization. Expert Syst Appl 36(1):616–624

    Article  Google Scholar 

  31. Chang YC, Chang CP, Chiu CH, Chen YC, Chu CP (2005) Constructing a SCORM-compliant intelligent strategy repository. Lect Notes Comput Sci (LNCS) 3583:157–162

    Article  Google Scholar 

  32. Baker FB (1992) Item response theory: parameter estimation techniques. Dekker, New York,

    MATH  Google Scholar 

  33. Hambleton RK, Swaminathan H, Rogers HJ (1991) Fundamentals of item response theory. Sage, Thousand Oaks

    Google Scholar 

  34. Holland JH (1975) Adaptation in natural and artificial system. The University of Michigan Press, Ann Arbor

    Google Scholar 

  35. Chang YC, Kao WY, Chu CP, Chiu CH (2009) A learning style classification mechanism for e-learning. Comput Educ. doi:10.1016/j.compedu.2009.02.008

    Google Scholar 

  36. Moodle (2007) Modular Object-Oriented Dynamic Learning Environment (MOODLE). [Online]. Available: http://moodle.org/

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan, Taiwan

    Chih-Ping Chu, Yi-Chun Chang & Cheng-Chang Tsai

Authors
  1. Chih-Ping Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi-Chun Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cheng-Chang Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi-Chun Chang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chu, CP., Chang, YC. & Tsai, CC. PC2PSO: personalized e-course composition based on Particle Swarm Optimization. Appl Intell 34, 141–154 (2011). https://doi.org/10.1007/s10489-009-0186-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-009-0186-7

Keywords

Search

Navigation