Jan Vleeshouwers

Learn More
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(More)
In the emerging field of educational data mining, a strong bias towards data-rich digital learning environments is the current state of affairs [2, table 2]. However, in many educational institutes a lot of regular course data will probably be more readily available. This data may also be used to support and advise students in various ways, for the better(More)
Supporting academic success is a central focus of higher education institutions. To address this challenge, predictive techniques could be applied in order to build models that predict academic performance such as student retention and graduation. This paper presents a predictive system for modeling and scoring students achievements. Based on student(More)
  • 1