Predicting Students’ Academic Dropout Using Artificial Neural Network


Series: Education in a Competitive and Globalizing World
BISAC: EDU027000

The student dropout phenomenon affects every level of the educational system in countries all over the world, including the most socio-economically developed ones. Success in education is crucial for jobs, productivity, and growth. Low levels and low completion rates create a skills bottleneck in the economic sectors and inhibit innovation, productivity, and competitiveness. The early detection of the phenomenon, at all levels of education, and the deepening of the causes that determine it are necessary prerequisites for any initiative aimed at reducing the factors affecting the decrease of the rates of formative failures and dropout at all levels.

This book aims to contribute to the continuing debate on the possibilities of how to improve educational processes with the help of data mining techniques. The book closes with a fascinating application of the principles of Artificial Neural Networks to help students at risk of university dropping out. It presents the case study of the dropout phenomenon in many degree programs at the University of Genoa (Italy). The analysis model, at the moment experienced on the Genoa University reality, is proposed as a simple and flexible instrument to support the activities of monitoring and evaluation of all the educational systems, an instrument available to decision-makers in identifying programs and strategies to support the persistency and the success in educational systems, but also to the families, in the recognition of the problems in which the intervention of the family, accompanying or not the institution, is decisive. I hope that this book will be useful for educators, counsellors, administrative staff, and researchers alike.
(Imprint: Nova)

Table of Contents

Table of Contents



Chapter 1 – Dropout Phenomenon at Universities (pp. 1-58)

Chapter 2 – The Determinants of University Dropouts (pp. 59-66)

Chapter 3 – Application of Artificial Neural Networks Modeling in Educational Research (pp. 67-98)

Chapter 4 – Towards the Construction of a Predictive Model for Students at Risk of Univesity Dropping Out (pp. 99-148)


Audience: For educators, counsellors, administrative staff, and researchers.

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