• I. O. AWOYELU Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria.
  • I. A. AGBOOLA Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria.
Keywords: Learning Disabilities, Classification, Principal Component Analysis (PCA), Multilayer Perceptron Network (MLP)


Learning disability is a general term that describes specific kinds of learning problems.  Although, Learning Disability cannot be cured medically, there exist several methods for detecting learning disabilities in a child. Existing methods of classification of learning disabilities in children are binary classification – either a child is normal or learning disabled. The focus of this paper is to extend the binary classification to multi-label classification of learning disabilities. This paper formulated and simulated a classification model for learning disabilities in primary school pupils. Information containing the symptoms of learning disabilities in pupils were elicited by administering five hundred (500) questionnaire to teachers of Primary One to Four pupils in fifteen government owned elementary schools within Ife Central Local Government Area, Ile-Ife of Osun State. The classification model was formulated using Principal Component Analysis, rule based system and back propagation algorithm. The formulated model was simulated using Waikatto Environment for Knowledge Analysis (WEKA) version 3.7.2. The performance of the model was evaluated using precision and accuracy. The classification model of primary one, primary two, primary three and primary four yielded precision rate of 95%, 91.18%, 93.10% and 93.60% respectively while the accuracy results were 95.00%, 91.18%, 93.10% and 93.60% respectively. The results obtained showed that the developed model proved to be accurate and precise in classifying pupils with learning disabilities in primary schools. The model can be adopted for the management of pupils with learning disabilities.



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