A Genetic Algorithm Based Method of Early Warning Rule Mining for Student Performance Prediction

摘要

Prediction of student failure in course learning has become a very difficult issue due to the large number of factors that can affect student's low performance, and it is difficult to use classical statistical methods because the results are usually very difficult to being understood by end-user. In this study, a genetic algorithm approach is proposed to deal with these problems using a data set of 576 higher education students' course learning information. Firstly, a mechanism of chromosome encoding is designed to represent associated individual namely classification rule. Secondly, a flexible fitness function is proposed in order to evaluate the quality of each individual, which can make a trade-off between sensitivity and specificity. Thirdly, a set of genetic operators including selection, crossover and mutation are constructed to generate offspring from the fittest individuals so as to select out the best solution to our problem, which can be easily used as an early warning rule to predict student failure in course learning. Finally, by testing the model, consistency was shown between the predicted results and the observed data, indicating that the employed method is promising for identifying at-risk students. The interpretable result is a significant advantage over other classical methods as it can obtain a both accurate and comprehensible classifier for student performance prediction.