Abstract

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.

Highlights

  • A child may be a slow learner for various reasons, including: heredity, inadequate brain development due to lack of stimulation, low motivation, attention problems, behavior problems, different cultural background from that which dominates in the school, or distracting personal problems[1,2,3,4]

  • The results of this paper are based on random sample of data of the slow learners of elementary school in Kuwait

  • The system allows repeating the material to the slow learners in order to enhance their academic achievement based on scientific recommendation for teaching slow learners

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Summary

INTRODUCTION

A child may be a slow learner for various reasons, including: heredity, inadequate brain development due to lack of stimulation, low motivation, attention problems, behavior problems, different cultural background from that which dominates in the school, or distracting personal problems[1,2,3,4]. Rather than developing special curricula are focusing their attention on the devising of better and more effective methods and techniques for use in teaching the regular curricula to slow learners [5]. The educational knowledge discovery and data mining techniques certainly can help through the discovery of hidden valuable knowledge to understand more about slow learner students and their educational behaviors. This paper discusses the discovery of the optimal pattern of learning for elementary school slow learner students through applying two machine learning clustering algorithms Expectation maximization and K-Mean. Section 3; go through the slow learner students definition, characteristics, suitable strategies of teaching and recommendation.

LITERATURE RELATED WORK
Results
TRADITIONAL LEARNING VERSUS E-LEARNING
CLUSTERING
Bottom Level
Middle Level
Top Level
EXPERIMENTAL RESULTS & DISSCUSSION
C A D B
VIII. CONCLUSION

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