Current trends in graduation rates show that 39% of young adults on average across OECD countries are expected to complete tertiary-type A (university level) education during their lifetime. According to Eurostat in 2017, an average of 10.6% of young people (aged 18-24) in the EU-28 were early leavers from education and training. Over 3 million young people in the European Union had been to university or college but had discontinued their studies at some point in their life, according to a survey of 2016. Therefore, the dropout level could potentially represent one of the major issues to be faced in a near future in the European Union. The main aim of the research is to predict, as early as possible, which kind of student will more easly dropout from the Higher Education (HE). This information would allow one to effectively carry out targeted actions in order to limit the incidence of the phenomenon. Today, Artificial Intelligence (AI) is being employed to replace human activities that are repetitive, e.g. in the autonomous driving field or for the image classification task. In these areas AI competes with man with quite satisfactory results and, in the case of HE dropout, it is extremely unlikely that an expert teacher will be able to "predict" the student's educational success based only on the data provided by administrative offices. The recent breakthrough on Neural Networks with the use of Convolutional Neural Networks (CNN) architectures has become disruptive in AI. By stacking together tens or hundreds of convolutional neural layers, a deep network structure is obtained, which has been proved very effective in producing high accuracy models. In this study the administrative data of approximately 6,000 students enrolled from 2009 on in the Education Department at Rome Tre University were used to train the CNNs. Then, the trained network provides a probabilistic model that indicates, for each student, the probability of dropping out. We used several types of state-of-the-art CNNs, and their variants, in order to build the most accurate model for the dropout prediction. The accuracy of the obtained models ranged from 67.1% for the students at the beginning of the first year up to 88.7% for the students at the end of the second year of their academic career. With the use of more data, for example students’ career data, we could develop more accurate dropout prediction models.
Mezzini, M., Bonavolonta', G., Agrusti, F. (2019). PREDICTING UNIVERSITY DROPOUT BY USING CONVOLUTIONAL NEURAL NETWORKS. In IATED 2019 Proceedings (pp.9155-9163).
PREDICTING UNIVERSITY DROPOUT BY USING CONVOLUTIONAL NEURAL NETWORKS
Mauro Mezzini
;Gianmarco Bonavolontà;Francesco Agrusti
2019-01-01
Abstract
Current trends in graduation rates show that 39% of young adults on average across OECD countries are expected to complete tertiary-type A (university level) education during their lifetime. According to Eurostat in 2017, an average of 10.6% of young people (aged 18-24) in the EU-28 were early leavers from education and training. Over 3 million young people in the European Union had been to university or college but had discontinued their studies at some point in their life, according to a survey of 2016. Therefore, the dropout level could potentially represent one of the major issues to be faced in a near future in the European Union. The main aim of the research is to predict, as early as possible, which kind of student will more easly dropout from the Higher Education (HE). This information would allow one to effectively carry out targeted actions in order to limit the incidence of the phenomenon. Today, Artificial Intelligence (AI) is being employed to replace human activities that are repetitive, e.g. in the autonomous driving field or for the image classification task. In these areas AI competes with man with quite satisfactory results and, in the case of HE dropout, it is extremely unlikely that an expert teacher will be able to "predict" the student's educational success based only on the data provided by administrative offices. The recent breakthrough on Neural Networks with the use of Convolutional Neural Networks (CNN) architectures has become disruptive in AI. By stacking together tens or hundreds of convolutional neural layers, a deep network structure is obtained, which has been proved very effective in producing high accuracy models. In this study the administrative data of approximately 6,000 students enrolled from 2009 on in the Education Department at Rome Tre University were used to train the CNNs. Then, the trained network provides a probabilistic model that indicates, for each student, the probability of dropping out. We used several types of state-of-the-art CNNs, and their variants, in order to build the most accurate model for the dropout prediction. The accuracy of the obtained models ranged from 67.1% for the students at the beginning of the first year up to 88.7% for the students at the end of the second year of their academic career. With the use of more data, for example students’ career data, we could develop more accurate dropout prediction models.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
