Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming

  • Authors

    • Marina Yusoff
    • Muhammad Najib Bin Fathi
    • . .
    2018-08-13
    https://doi.org/10.14419/ijet.v7i3.15.17408
  • Clustering, Hierarchical Clustering, K-Means, Learning Style, Neuro Linguistic Programming
  • Students’ performance is a key point to get a better first impression during a job interview with an employer. However, there are several factors, which affect students’ performances during their study. One of them is their learning style, which is under Neurolinguistic Programming (NLP) approach. Learning style is divided into a few behavioral categories, Visual, Auditory and Kinesthetics (VAK). This paper addresses the evaluation of clustering methods for the identification of learning style based on system preferences. It starts with the distribution of questionnaires to acquire the information on the VAK for each student. About 167 respondents in the Faculty of Computer and Mathematical Science are collected. It is then pre- processed to prepare the data for clustering method evaluations. Three clustering methods; Simple K-Mean, Hierarchical and Density-Based Spatial Clustering of Applications with Noise are evaluated. The findings show that Simple K-Mean offers the most accurate prediction. Upon completion, by using the dataset, Simple K-Means technique estimated four clusters that yield the highest accuracy of 74.85 % compared to Hierarchical Clustering, which estimated four clusters and Density- Based Spatial Clustering of Applications with Noise which estimated three clusters with 52.69% and 61.68 % respectively. The clustering method demonstrates the capability of categorizing the learning style of students based on three categories; visual, auditory and kinesthetic. This outcome would be beneficial to lecturers or teachers in university and school with an automatically clustering the students’ learning style and would assist them in teaching and learning, respectively.

     

     
  • References

    1. [1] Ali, S., Haider, Z., Munir, F., Khan, H., & Ahmed, A. (2013). Factors Contributing to the Students Academic Performance: A Case Study of Islamia University Sub-Campus. America Journal of Educational Research, 1(8), 283-289.

      [2] Curry, L. (1987). Integrating concepts of cognitive or learning style: A review with attention to psychometric standards. Ottawa, ON: Canadian College of Health Service Executives.

      [3] Reid, J. M. (1987). The Learning Style Preferences of ESL Students. TESOL Quarterly, 21(1),87

      [4] Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1-13.

      [5] Brown, H. D. (2000). Principles of language teaching and learning, (4th ed.). White Plains, NY: Longman.

      [6] O'Connor, J., & Seymour, J. (2011). Introducing NLP: Psychological skills for understanding and influencing people. Conari Press.

      [7] Kanar, C. C. (1995). The confident student. Boston: Houghton Mifflin Company.

      [8] Krishna, T. S., Babu, A. Y., & Kumar, R. K. (2018). Determination of Optimal Clusters for a Non-hierarchical Clustering Paradigm K-Means Algorithm. In Proceedings of International Conference on Computational Intelligence and Data Engineering (pp. 301-316). Springer, Singapore.

      [9] Ooi, T. H., Ngah, U. K., Khalid, N. E. A., & Venkatachalam, P. A. (2000). Mammagraphic Calcification Clusters Using The Region Growing Technique. In Proceedings of The New Millennium International Conference on Pattern Recognition, Image Processing and Robot Vision (pp. 157-163).

      [10] Seman, A., Bakar, Z. A., & Isa, M. N. (2012). An efficient clustering algorithm for partitioning Y-short tandem repeats data. BMC research notes, 5(1), 557.

      [11] Pindah, W., Nordin, S., Seman, A., & Said, M. S. M. (2015, April). Review of dimensionality reduction techniques using clustering algorithm in reconstruction of gene regulatory networks. In Computer, Communications, and Control Technology (I4CT), 2015 International Conference on (pp. 172-176). IEEE.

      [12] Seman, A., Bakar, Z. A., Sapawi, A. M., & Othman, I. R. (2013). A Medoid-based Method for Clustering Categorical Data. Journal of Artificial Intelligence, 6(4), 257-265.

      [13] Khalid, N. E. A., Yusoff, M., Kamaru-Zaman, E. A., & Kamsani, I. I. (2014). Multidimensional Data Medical Dataset Using Interactive Visualization Star Coordinate Technique. Procedia Computer Science, 42, 247-254.

      [14] Raja, R., & Tien, N. (2018). Exploring multi-modality tools of Neuro-Linguistic Programming (NLP) to facilitate better learning among Primary School students.

      [15] Bajorski, P., & Bajorski, P. (2011). Clustering – Unsupervised Learning. In Statistics for

      Imaging, Optics, and Photonics (pp. 297-327): John Wiley & Sons, Inc.

  • Downloads

  • How to Cite

    Yusoff, M., Najib Bin Fathi, M., & ., . (2018). Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming. International Journal of Engineering & Technology, 7(3.15), 63-67. https://doi.org/10.14419/ijet.v7i3.15.17408