DATA MINING IN DEDUCING LEARNING GAPS AND EXAM FAILURE AMONG STUDENTS
Data mining is a process of extracting viable information and knowledge, which is important in the processing of large data sets. Many applications of data mining techniques in the field of education address issues related to predictive efficiency, mobility and maintaining the number of students; clustering – students’ typology and segmentation, predictive modeling of academic performance etc. According to recent trends, the number of students has increased in several faculties and departments in the institutions of higher education - the city of Cluj-Napoca, a region with fast economic and social development, having one of the largest number of students in Romania. A major concern in the higher education institutions is to deduct and understand learning gaps and exam failure in order to maintain and increase the number of students, and to attract them to continue their studies. Following this, the authors present their considerations and ideas based on data mining methods using data from the students of The Faculty of Economics and Business Administration, Cluj-Napoca.