Predicting Students Drop Out: A Case Study
نویسندگان
چکیده
The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific to the EE program. Our experimental results show that rather simple and intuitive classifiers (decision trees) give a useful result with accuracies between 75 and 80%. Besides, we demonstrate the usefulness of cost-sensitive learning and thorough analysis of misclassifications, and show a few ways of further prediction improvement without having to collect additional data about the students.
منابع مشابه
Exploiting Academic Records for Predicting Student Drop Out: a case study in Brazilian higher education
Students’ drop out is a major concern of the Brazilian higher education institutions as it may cause waste of resources and decrease graduation rates. The early detection of students with high probability of dropping out, as well as understanding the underlying causes, are crucial for defining more effective actions toward preventing this problem. In this article, we cast the drop out detection...
متن کاملUnderstanding Why IS Students Drop Out: Toward A Process Theory
IT students dropping out is a key problem in academic institutions worldwide. Previous research on student dropout has advanced many factor or variance models explaining or predicting why university student drop out. Although these studies increased our understanding of the reasons students drop out of computer science courses, university studies, and online learning, we find the factor or vari...
متن کاملPredicting drop-out from social behaviour of students
This paper focuses on predicting drop-outs and school failures when student data has been enriched with data derived from students social behaviour. These data describe social dependencies gathered from e-mail and discussion board conversations, among other sources. We describe an extraction of new features from both student data and behaviour data represented by a social graph which we constru...
متن کاملPredicción del Fracaso Escolar Mediante Técnicas de Minería de Datos
This paper proposes to apply data mining techniques to predict school failure and drop out. We use real data on 670 middle-school students from Zacatecas, México and employ white-box classification methods such as induction rules and decision trees. Experiments attempt to improve their accuracy for predicting which students might fail or drop out by: firstly, using all the available attributes;...
متن کاملFactor Analysis with Data Mining Technique in Higher Educational Student Drop Out
The increase of students’ drop out rate in higher education is one of the important problems in most institutions. The discovery of hidden knowledge from the educational data system by the effective process of data mining technology to analyze factors affecting student drop out can lead to a better academic planning and management to reduce students drop out rate, as well as can inform valuable...
متن کامل