A parallel intelligent algorithm applied to predict students dropping out of university

Abstract

A student dropping out of university means that he/she quits the university early. Increasingly more students are dropping out of university, the reasons for which vary. It is an important issue for universities to predict students wanting to drop out in advance. Such information would allow them to find useful strategies to help university students and prevent them from dropping out. Compared with all students at a university, student dropping out is a relatively rare event. This represents an issue of imbalanced data. In such data, the majority of classes have more instances than do minority classes. Conventional algorithms classify the minority classes into majority classes and then ignore the minority classes. When data grow with imbalanced features, it becomes difficult to solve these problems with conventional algorithms. An algorithm is proposed to predict students dropping out of a university. In this algorithm, a parallel framework based on Apache Spark with three approaches is presented to parallel process the data on students dropping out of a university. Thereafter, the improved bacterial foraging optimization (BFO) and ensemble method are used to improve the classification execution. This technique is applied to a real scenario from a university in Taiwan. The dataset taken from the UCI machine learning repository is also used to verify the correctness of the introduced parallel intelligent algorithm. The error rate for students dropping out is 7.65% for this algorithm, which shows that the proposed algorithm surpasses the performance of the compared techniques. The outcomes of the suggested algorithm will provide useful information for decision making.