ABSTRACT

Educational data mining (EDM) is a field that concentrates on prediction and is known for its role

in uncovering hidden information from large volume of data. EDM has seen an emergence of

research leading to strategies that aim to address issues of higher education with primary focus on

students’ attrition. An unceasingstudent’s attrition from the undergraduate programmeof Ahmadu

Bello University, Zaria (A.B.U, Zaria) has significant ramification for individuals affected by this

problem and the society in general. This work examines the problem of predicting student’s

dropout in selected programmes of A.B.U Zaria. This work also focuses on addressing students’

attrition by creating a first year at risk model to be used in early detection of students at the risk of

attrition. Various factors that might influence students’ attrition were collected from two (2)

primarysources (student portal and exam processingsoftware) of A.B.U Zariainformation system.

This work applies variants of existing classification algorithms in building predictive models; also

an instance based learning algorithm (k nearest neighbor) was chosen and modified. An initial data

preprocessing, feature selection and a 10-fold cross validation experiment was carried out in the

model development phase of this work using WEKA (an open source tool for data mining tool).

The classification algorithms employed in this work include: Multi-Layer Perceptron, Naïve

Bayes, J48 Decision Tree, Sequential minimal optimization, K-Nearest Neighbor and a modified

K-Nearest Neighbor. Results obtained showed J48 decision tree algorithm have performed nicely

among all, based on our dataset with an average accuracy of 97.9%. Also the modified nearest

neighbor algorithm performed next with an average accuracy of 97.3%. The result obtained were

validated and analyzed using WEKA’s experimenter. To choose the best model, we conducted a

comparative analysis of the classifiers used in this dissertation work.

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