PREDICTING MATHEMATICS 1 COURSE SUCCESS BY USING HIERARCHICAL ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM

Predicting mathematics 1 course success of students is very important to prepare them before the semester. it is difficult to obtain solution because of the non-linear property of data set. fuzzy logic is one of the common methods for the problems which involve numeric values. in fuzzy logic, it is important to determine membership functions and their parameter’s values correctly. this can be done by an expert or can be learned with a data set. in this study, we aimed to predict the mathematics 1 course success of 434 students who enrolled to engineering faculty of pamukkale university in 2007-2008 academic year by using their university exam data. for this, the adaptive-network-based fuzzy inference system (anfis) which combines the important characteristics of artificial neural network and fuzzy logic was used. in training section, nine parameters which are selected from sixteen parameters in data set with different combinations were given to the anfis. when an anfis structure with nine input parameters has at least three membership functions for each input, it will have at least 3^9 fuzzy rules. because of this, the training part is too slow and too much memory is needed. instead of this inefficient structure, a hierarchical method was proposed. in this method, the anfis is partitioned to the sub-systems. each sub-system performs some part of input parameters and sends their result to the final anfis structure to obtain the overall system output. after testing with one-third of data set, two best prediction results with ratio 77.77% and 78.47% are obtained. when these results are analyzed, it is seen that 64 successful students from 85 students and 48 unsuccessful students from 59 students in mathematics 1 course were predicted truly in the result with ratio 77.77%. similarly, 69 successful students from 85 students, and 44 unsuccessful students from 59 students were predicted truly in the result with ratio 78.47%.