Skip to main content
SpringerLink
Log in

Adaptive Content Sequencing for e-Learning Courses Using Ant Colony Optimization

  • Conference paper
Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 131))

Abstract

Learners often get overwhelmed by the availability of large volumes of learning content in web-based educational systems. This content is presented to the learners either statically or with numerous hyper-links for navigation. The choice of content varies according to the requirements. Hence a static sequence of contents cannot satiate different learners enrolled in a course. Sequencing content according to the learners’ needs is the objective of designing adaptive systems. Ant Colony Optimization (ACO) is an evolutionary technique that takes into account the dynamic nature of the problem and employs collective intelligence to provide optimized solutions. An ACO based algorithm Adaptive Content Sequencing in eLearning (ACSeL) is proposed in this paper that evaluates the level of a learner and recommends appropriate concepts to him/her. It is sensitive to the changes in learning behaviours of each learner and fine-tunes its strategies to recommend the next concept accordingly. The behaviours of past learners are captured and utilized to recommend content to prospective learners.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Sharma, R., Banati, H., Bedi, P.: Analyzing user interaction to design an intelligent e-learning environment. Int. J. E Learning 10(4), 441–460 (2011)

    Google Scholar 

  2. Gutiérrez, S., Pardo, A., Kloos, C.D.: An Adaptive Tutoring System based on Hierarchical Graphs. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 401–404. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  3. Heylighen, F.: Collective Intelligence and its implementation on the Web: algorithms to develop a collective mental map. Computational & Mathematical Organization Theory 5, 1–26 (1999)

    Article  Google Scholar 

  4. Singh, V.K., Gautam, D., Singh, R.R., Gupta, A.K.: Agent-based computational modeling of emergent collective intelligence. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 240–251. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  5. Bedi, P., Sharma, R.: Trust based recommender system using ant colony for trust computation. Expert Systems with Applications (2011) (article in Press)

    Google Scholar 

  6. Bedi, P., Mediratta, N., Dhand, S., Sharma, R., Singhal, A.: Avoiding traffic jam using Ant Colony Optimization – a novel approach. In: Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), vol. 1, pp. 61–67 (2007)

    Google Scholar 

  7. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1, 53–66 (1997)

    Article  Google Scholar 

  8. Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society 26, 29–41 (1996)

    Article  Google Scholar 

  9. Sharma, R., Banati, H., Bedi, P.: Social support based multi-agent framework for designing an e-learning course. In: Proceedings of the 2011 IEEE Third International Conference on Communication Software and Networks (ICCSN), pp. 160–164 (2011)

    Google Scholar 

  10. Maycock, K.: A Framework for Adaptive e-Learning. Ph.D. Thesis, Department of Computer Science, National University of Ireland, Maynooth, Co. Kildare, Ireland (2010)

    Google Scholar 

  11. Brusilovsky, P.: Adaptive Hypermedia. Int. J. User Modeling and User-Adapted Interaction 11(1/2), 87–110 (2001)

    Article  Google Scholar 

  12. De Bra, P., Aroyo, L., Cristea, A.: Adaptive Web-based Educational Hypermedia. In: Levene, M., Poulovassilis, A. (eds.) Web Dynamics, Adaptive to Change in Content, Size, Topology and Use, pp. 387–410. Springer (2004)

    Google Scholar 

  13. Henze, N., Nejdl, W.: A logical characterization of adaptive educational hypermedia. New Review of Hypermedia and Multimedia 10, 77–113 (2004)

    Article  Google Scholar 

  14. Idris, N., Yusof, N., Saad, P.: Adaptive course sequencing for personalization of learning path using Neural Network. Int. J. Advance. Soft Computing 1, 49–61 (2009)

    Google Scholar 

  15. de Marcos, L., Martinez, J.J., Gutiérrez, J.A., Barchino, R., Gutiérrez, J.M.: A new sequencing method in web-based education. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2009), pp. 3219–3225 (2009)

    Google Scholar 

  16. Sarath Chandar, A., Dheeban, S., Deepak, V., Elias, S.: Personalized e-course composition approach using digital pheromones in improved particle swarm optimization. In: Proceedings of the 2010 Sixth International Conference on Natural Computation (ICNC), pp. 2677–2681 (2010)

    Google Scholar 

  17. Yang, Y., Wu, C.: An attribute-based ant colony system for adaptive learning object recommendation. Expert Systems with Applications 36, 3034–3047 (2009)

    Article  Google Scholar 

  18. Wong, L.H., Looi, C.K.: Swarm Intelligence: new techniques for adaptive systems to provide learning support. Interactive Learning Environments 10, 1–22 (2010)

    Google Scholar 

  19. Munoz-Organero, M., Ramirez, G.A., Merino, P.M., Kloos, C.D.: Analyzing convergence in e-learning resource filtering based on ACO techniques: a case study with telecommunication engineering students. IEEE Transactions on Education 53, 542–546 (2010)

    Article  Google Scholar 

  20. Semet, Y., Jamont, Y., Biojout, R., Lutton, E., Collet, P.: Artificial ant colonies and e-learning: an optimisation of pedagogical paths. In: Proceedings of the Tenth International Conference on Human-Computer Interaction (2003)

    Google Scholar 

  21. Gutiérrez, S., Valigiani, G., Collet, P., Kloos, C.D.: Adaptation of the ACO heuristic for sequencing learning activities. In: Proceedings of the European Conference on Technology Enhanced Learning (EC-TEL), pp. 17–20 (2007)

    Google Scholar 

  22. Janssen, J., Tattersall, C., Waterink, W., van denberg, B., van Es, R., Bolman, C., Koper, R.: Self-organising navigational support in lifelong learning: how predecessors can lead the way. Computers & Education 49, 781–793 (2007)

    Article  Google Scholar 

  23. Wang, T., Wang, K., Huang, Y.: Using a style-based ant colony system for adaptive learning. Expert Systems with Applications 34, 2449–2464 (2008)

    Article  Google Scholar 

  24. Sharma, R., Banati, H., Bedi, P.: Incorporating social opinion in content selection for an e-learning course. In: Proceedings of the Sixth International Conference on Computer Science & Education (ICCSE 2011), pp. 1027–1032 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Delhi, Delhi, India

    Richa Sharma & Punam Bedi

  2. Dyal Singh College, University of Delhi, Delhi, India

    Hema Banati

Authors
  1. Richa Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hema Banati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Punam Bedi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Richa Sharma .

Editor information

Editors and Affiliations

  1. , Department of Mathematics, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Kusum Deep

  2. Department of Mathematics and, Computer Science, Liverpool Hope University, Hope Park, Liverpool, L16 9JD, United Kingdom

    Atulya Nagar

  3. , Department of Paper Technology, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Millie Pant

  4. Information Technology and Management, ABV-Indian Institute of, 109, E- Block, Gwalior, 474010, India

    Jagdish Chand Bansal

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer India Pvt. Ltd.

About this paper

Cite this paper

Sharma, R., Banati, H., Bedi, P. (2012). Adaptive Content Sequencing for e-Learning Courses Using Ant Colony Optimization. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_53

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-0491-6_53

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-0490-9

  • Online ISBN: 978-81-322-0491-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation