engleski

Identification of Features Associated with University Dropout-a case study of University of Split, Faculty of Maritime Studies

The primary goal of higher education institutions is to provide a quality educational process. Maritime higher education is an essential element in acquiring the knowledge, and skills needed on board a ship. One of the indicators of potential problems in this educational process may be a high number of dropouts in the early years. Predicting student success and dropout, or identifying students who are at higher risk for dropping out, is critical to improving the quality of higher education. An analysis of the academic performance of students at the University of Split, Faculty of Maritime Studies (PFST) was conducted a high dropout rate was revealed. This research aims to improve early prediction of student dropout by identifying the most relevant features. The data is processed and the features that influence dropout are extracted through an attribute selection algorithm and machine learning techniques such as decision trees. The results of our research suggest that higher education institutions should be aware of the need to identify early the profile of students who are at potential risk of dropping out. Moreover, the developed model is useful for strategic planning of additional mechanisms to improve the efficiency of study at maritime higher education institutions.

maritime higher education ; dropout ; machine learning ; feature selection ; Faculty of Maritime Studies Split

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