Fuzzy rule interpolation based model for student result prediction

Allocating university resources, especially defining the number of necessary student groups and laboratory classes is a hard task without knowing the exact number of students who will enroll in the given courses. This number usually depends on the exam results of the prerequisite courses. However, the planning of the next term has to be done some months before the end of the actual term. This paper presents the creation of a fuzzy model that can predict the student results in case of the Visual Programming course with an acceptable accuracy based on nine input factors describing the relevant history of the student. The model has a low complexity rule base containing only 28 rules and predicts the exam result using fuzzy rule interpolation based inference. The position of the rule consequent sets as well as the rule weights were tuned by particle swarm optimization. The root mean squared error expressed in percentage of the output range was less than 13% in case of all the training, validation and test datasets, which gives a satisfactory level of information for the planning of the number of student groups and laboratory classes in the next term in case of the next course that follows the examined Visual Programming course.