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IJSTR >> Volume 9 - Issue 4, April 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Personalized Learning Approach In Learning Management System Using Cluster Models

[Full Text]



Kirk Alvin S. Awat, Maria Rona L. Perez, Ace C. Lagman, Tim Jamison S. Awat, John Benedict C. Legaspi



data mining, item analysis, item bank, K-Means clustering, reclassification



This paper focuses on the integration of cluster models using K-means algorithm as a main mechanism in providing a personalized learning approach integrated in a learning management system (LMS). The cluster models provide intelligent questions mechanism suited to the individualized learning of students. The LMS will provide necessary examinations to gauge the students’ learning capability and based on the result of the examination, the item analysis and clustering algorithm were performed to provide reinforcement questions in helping students to pass a certain examination. The derived cluster models consist of easy, average, and hard question categories. The process repeats until such time that the students will be able to answer the questions correctly. In this way, the students increase their academic performance. The system was also evaluated by the LMS experts using the FURPS model. Overall, the system was rated with a “Very Satisfactory” rating.



[1] D. Walker, J. Linder, T. P. Murphey and K. Dooley. Learning Management Usage Perspective from University Instructors, The Quarterly Review of Distance Education, vol. 17, pp. 41-50, 2016.
[2] R. Cornish. Cluster Analysis, 2007. [Online]. Available: http://www.statstutor.ac.uk/resources/uploaded/clusteranalysis.pdf.
[3] B. Foley. An Introduction to Cluster Analysis, 15 February 2018. [Online]. Available: https://www.surveygizmo.com/resources/blog/cluster-analysis/. [Accessed July 2019].
[4] Maseleno, M. Huda, N. Sabani and B. Basiron. Demystifying Learning Analytics in Personalised Learning, International Journal of Engineering and Technology, 2018.
[5] Berkhin. A Survey of Clustering Data Mining Techniques in Grouping Multidimensional Data, Berlin, Heidelberg, Springer, 2006.
[6] Barbosa H., Garica F. and Rodriguez, M. Construction of Assessments with double adaptation processes. Innovations in E-Learning Instructional Technology Assessment and Engineering, Iskander (ed),pp. 133-13
[7] M. Ballera, I. A. Lukandu and A. Radwan. Improving Learning Throughput in E-learning using Interactive-Cognitive Based Assessment, The International Journal of E-Learning and Educational Technologies in the Digital Media, vol. 1, no. 1, pp. 32-49, January 2015.