Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Modeling and Simulation of K-Means Clustering Learning Object Adaptability Model for Selecting Materials in E-Learning

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Awoyelu I.O., Awosan O.A., Adagunodo E.R.
10.5120/ijca2016908822

Awosan O A Awoyelu I.O. and Adagunodo E.R.. Modeling and Simulation of K-Means Clustering Learning Object Adaptability Model for Selecting Materials in E-Learning. International Journal of Computer Applications 141(1):10-18, May 2016. BibTeX

@article{10.5120/ijca2016908822,
	author = {Awoyelu I.O., Awosan O.A. and Adagunodo E.R.},
	title = {Modeling and Simulation of K-Means Clustering Learning Object Adaptability Model for Selecting Materials in E-Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2016},
	volume = {141},
	number = {1},
	month = {May},
	year = {2016},
	issn = {0975-8887},
	pages = {10-18},
	numpages = {9},
	url = {http://www.ijcaonline.org/archives/volume141/number1/24747-2016908822},
	doi = {10.5120/ijca2016908822},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The delivery of adaptive instructional materials to learners is a good way of achieving effectiveness in learning session. Fitting teaching material varies from one student to another based on students’ knowledge level, that is, cognitive status and ability to learn. This study proposes a model for selecting appropriate learning materials to learners. The proposed model was formulated using the K–means Clustering Algorithm and represented using Unified Modelling Language (UML). The adaptive model was simulated using the K-Means Clustering Algorithm to train and test the adaptive model using the historical data collected from a developed e –Learning system for Undergraduate students taking Introduction to FORTRAN programming Language course. The evaluation results showed that the system had precision of 0.7143, 0.6667, 1.000 and 0.2942 for learning object 1, learning object 2, learning object 3 and learning object 4 respectively. Also, the recall results were 0.6250, 0.8889, 0.6667 and 0.3864 for learning object 1, learning object 2, learning object 3 and learning object 4 respectively. The system can be used to effectively and successfully assign learning materials to learners based on their cognitive level.

References

  1. Como, L., and Snow, E. R. (1986). Adapting Teaching to Individual Differences among Learners. In M. C. Wittrock (Ed.), Handbook of Research on Teaching (3rd ed.). New York: Macmillan.
  2. Wang, M. and Lindvall, C. M. (1984). Individual Differences and School Learning Environments. Review of Research in Education, 11, 161–225.
  3. Glaser, R. (1977). Adaptive education: Individual, Diversity and Learning. New York: Holt.
  4. Carchiolo, V., Longheu, A., Malgeri, M., and Mangioni, G. (2003). Courses Personalization in an e- Learning Environment. In Proceedings of the 3rd IEEE International Conference on Advanced Learning Technologies, ICALT’03. July 9-11, 252-253.
  5. Licchelli, O., Basile, T.M., Di Mauro, N. and Esposito, F. (2004). Machine Learning Approaches for Inducing Student Models. In: 17th International Conference on Innovations in Applied Artificial Intelligence, IEA/AIE 2004. LNAI Vol. 3029. Springer-Verlag, Berlin Heidelberg New York, pp. 935-944.
  6. Yoo, J., Yoo, S., Lance, C. and Hankins, J. (2006). Student Progress Monitoring Tool Using Treeview. In the 37th Technical Symposium on Computer Science Education, SIGCSE’06. ACM Press. March 1-5, Houston, USA, pp. 373-377.
  7. Grieser, G., Klaus, P.J. and Lange, S. (2002). Consistency Queries in Information Extraction. In the Proceedings of the 13th International Conference on Algorithmic Learning Theory. Lecture Notes in Artificial Intelligence, Vol. 2533. Springer-Verlag, Berlin Heidelberg New York, 173-187.
  8. Hwang, G.J. (2003). A Test-Sheet-Generating Algorithm for Multiple Assessment Requirements. IEEE Transactions on Education, 46(3), 329-337.
  9. Mullier, D., Moore, D. and Hobbs, D. (2001). A Neural-Network System for Automatically Assessing Students. In: Kommers, P., Richards, G. (eds.): World Conference on Educational Multimedia, Hypermedia and Telecommunications, 1366-1371.
  10. Tang, T.Y. and McCalla, G. (2005). Smart Recommendation for an Evolving e-Learning System: Architecture and Experiment. International Journal on e-Learning, 4(1), 105-129.
  11. Teng, C., Lin, C., Cheng, S and Heh, J (2004). Analyzing User Behavior Distribution on e-Learning Platform with Techniques of Clustering. In the Society for Information Technology and Teacher Education International Conference, pp. 3052-3058.
  12. Hammouda, K. and Kamel, M. (2005). Data Mining in e-Learning. In: Pierre, S. (ed.): e-Learning Networked Environments and Architectures: A Knowledge Processing Perspective. Springer-Verlag, Berlin Heidelberg New York.
  13. Castro, F., Vellido, A., Nebot, A. and Minguillón, J. (2005). Detecting a Typical Student Behaviour on an e-Learning System. In: VI Congreso Nacional de Informática Educativa, Simposio Nacional de Tecnologías de la Información y las Comunicaciones en la Educación, SINTICE’2005. September 14-16, Granada, Spain, 153-160.
  14. Qing L., Shaochun Z., Peng W., Xiaozhuo G. and Xiaolin Q. (2010). Learner Model in Adaptive Learning System. Journal of Information and Computational Science, 7(5), pp 1137-1145.
  15. Maryam Y., Hossein J. and Abdel R. (2013). A Personalized Adaptive e-learning Approach Based on Semantic Web Technology. Webology, 10(2). Article 110, Available at: http://www.webology.org/2013/v10n2/a110.pdf

Keywords

Learning objects, Adaptive, Cognitive, K-means clustering, E-learning