Pregled bibliografske jedinice broj: 1096315
Web prediction framework for college selection based on the hybrid Case Based Reasoning model and expert's knowledge
Web prediction framework for college selection based on the hybrid Case Based Reasoning model and expert's knowledge // International Journal of Hybrid Intelligent Systems, 13 (2016), 3-4; 161-171 doi:10.3233/his-160233 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1096315 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Web prediction framework for college selection
based on the hybrid Case Based Reasoning model
and expert's knowledge
Autori
Trstenjaka, Bruno ; Donkob, Dzenana
Izvornik
International Journal of Hybrid Intelligent Systems (1448-5869) 13
(2016), 3-4;
161-171
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
Hybrid case based reasoning ; expert knowledge ; prediction, web framework
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo