Clustering of Slow Learners Behavior for Discovery of Optimal Patterns of Learning

with the increased rates of the slow learners (SL) enrolled in schools nowadays; the schools realized that the traditional academic curriculum is inadequate. Some schools have developed a special curricula that are particularly suited a slow learner while others are focusing their efforts on the devising of better and more effective methods and techniques in teaching. In the other hand, knowledge discovery and data mining techniques certainly can help to understand more about these students and their educational behaviors. This paper discusses the clustering of elementary school slow learner students behavior for the discovery of optimal learning patterns that enhance their learning capabilities. The development stages of an integrated E-Learning and mining system are briefed. The results show that after applying the clustering algorithms Expectation maximization and K-Mean on the slow learner’s data, a reduced set of five optimal patterns list (RSWG, RWSG, RWGS, GRSW, and SGWR) is reached. Actually, the students followed these five patterns reached grads higher than 75%. Therefore, the proposed system is significant for slow learners, teachers and schools.

@article{Mohammad2014,
  title     = {Clustering of Slow Learners Behavior for Discovery of Optimal Patterns of Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {5},
  number    = {11},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {Thakaa Z. Mohammad and Abeer M.Mahmoud},
  doi       = {10.14569/IJACSA.2014.051118},
  url       = {https://doi.org/10.14569/IJACSA.2014.051118}
}