Web prediction framework for college selection based on the hybrid Case Based Reasoning model and expert's knowledge

Abstract

Higher education today represents the basis of any successful society. Every day we are witnessing an increase in the number of HEI, an increase in the number of students but also an increase in the number of dropouts. This paper presents a new concept of the prediction framework which enables the selection of future college students based on their socio-demographic characteristics. The framework enables college autonomy in creating their own predictive models based on the characteristics of its students. In the prediction process, the framework has the ability of dynamic adjustment according to specific characteristics of each college. The framework is object-oriented and enables the performance of an online prediction process. The proposed framework uses a hybrid Case Based Reasoning (CBR) model and expert's knowledge. The hybrid CBR model has integrated several methods of machine learning: Information Gain, K-means and Case-based reasoning. The study used datasets collected from several colleges, a part of the Croatian Information System for Higher Education (ISVU). The case study demonstrates that our proposed web prediction framework is efficient and capable of providing very good results in the process of prediction. The achieved results provide guidelines for the future development of the prediction framework.