Predicting students’ dropout at university using Artificial Neural Networks

This study is part of an ongoing project investigating the first stage of the process of student transition to university. This paper aims to contribute to the continuing debates on the possibilities how to reduce the student failure and improve educational processes with the help of data mining techniques, in particular of the artificial neural networks. The population consists of 810 students enrolled for the first time in a health care professions degree course at the University of Genoa in the academic year 2008-09. The research is based on the analysis of data and information originating from primary sources: administrative data related to the careers of students; statistical data collected during the research through an ad hoc survey; data derived from telephone interviews with students who had not completed the enrolment in the subsequent years. The neural network correctly predicted 84 percent of the cases of group 1,81 percent of the cases of group 2, and 76 percent of the cases belonging to group of dropouts. The application of the artificial neural network model can offer a valid tool to design educational interventions to deliver to those who score high in the level of risk.

[1]  A. Cabrera,et al.  The targeting and packaging of student aid and its effect on attrition , 1988 .

[2]  Srinivasan Ragothaman,et al.  Using Neural Networks to Predict MBA Student Success. , 2004 .

[3]  M. R. Larsen,et al.  Dropout Phenomena at Universities: What is Dropout? Why does Dropout Occur? What Can be Done by the Universities to Prevent or Reduce it? , 2013 .

[4]  A. Waterman,et al.  RELATIONSHIP BETWEEN FRESHMAN EGO IDENTITY STATUS AND SUBSEQUENT ACADEMIC BEHAVIOR: A TEST OF THE PREDICTIVE VALIDITY OF MARCIA'S CATEGORIZATION SYSTEM FOR IDENTITY STATUS , 1972 .

[5]  Marek Wesolowski,et al.  Artificial neural networks: theoretical background and pharmaceutical applications: a review. , 2012, Journal of AOAC International.

[6]  E. Marks.  Student perceptions of college persistence, and their intellective, personality and performance correlates. , 1967, Journal of educational psychology.

[7]  Josef Havel,et al.  Cluster analysis and artificial neural networks multivariate classification of onion varieties. , 2010, Journal of agricultural and food chemistry.

[8]  Yulei Jiang,et al.  A multitarget training method for artificial neural network with application to computer-aided diagnosis. , 2012, Medical physics.

[9]  Amaury Nora,et al.  The Convergence between Two Theories of College Persistence , 1990 .

[10]  Vincent Tinto,et al.  From Theory to Action: Exploring the Institutional Conditions for Student Retention , 2010 .

[11]  Kenji Suzuki,et al.  Artificial Neural Networks - Methodological Advances and Biomedical Applications , 2011 .

[12]  Stanley I. Iwai,et al.  College attrition and the financial support systems of students , 1982 .

[13]  P. H. Zope,et al.  Importance of Artificial Neural Network in Medical Diagnosis disease like acute nephritis disease and heart disease , 2013 .

[14]  Vincent Tinto,et al.  Leaving College: Rethinking the Causes and Cures of Student Attrition. , 1988 .

[15]  Alessandro Pandini,et al.  Artificial neural networks for efficient clustering of conformational ensembles and their potential for medicinal chemistry. , 2013, Current topics in medicinal chemistry.

[16]  Fred L. Pincus.  The False Promises of Community Colleges: Class Conflict and Vocational Education , 1980 .

[17]  Qi-Jun Zhang,et al.  Neural Networks for RF and Microwave Design , 2000 .

[18]  Mukesh R. Patel,et al.  Design porosity osmotic tablet for delivering low and pH-dependent soluble drug using an artificial neural network. , 2012, Current drug delivery.

[19]  B. A. Kirk,et al.  Factors related to persistence and withdrawal among university students. , 1970 .

[20]  Kevin N. Gurney,et al.  An introduction to neural networks , 2018 .

[21]  Qeethara Al-Shayea.  Artificial Neural Networks in Medical Diagnosis , 2024, International Journal of Research Publication and Reviews.

[22]  P. Bentler,et al.  Models of attitude–behavior relations. , 1979 .

[23]  David H. Kamens,et al.  The College "Charter" and College Size: Effects on Occupational Choice and College Attrition. , 1971 .

[24]  Vincent Tinto.  Dropout from Higher Education: A Theoretical Synthesis of Recent Research , 1975 .

[25]  S. Schwartz,et al.  Leaving College: Rethinking the Causes and Cures of Student Attrition , 1987 .

[26]  John M. Braxton,et al.  Melding Organizational and Interactional Theories of Student Attrition: A Path Analytic Study , 2017 .

[27]  C. Ethington.  A psychological model of student persistence , 1990 .

[28]  Kevin W. Bowyer,et al.  Generating ROC curves for artificial neural networks , 1994, IEEE Transactions on Medical Imaging.