Design and Implementation of Faculty Support System to Reduce Course Dropout Rates

The primary goal of educational systems is not only to provide quality of education but also to make sure that students graduate with a strong academic standing. One specific challenge that universities face is high course drop rates. An early prediction of students’ failure may help to identify students who need special attention to reduce course drop rates by providing appropriate interventions, such as continuous mentoring and conducting review sessions. To address this problem, a new framework called Faculty Support System (FSS) is proposed that learns different classification models to predict student course performance based on his/her attendance, and performance in assignments, quizzes, in-class group projects, and exams. The investigated approaches for this task include Naïve Bayes, Multi-Layer Neural Networks, Decision Trees, and Random Forests. Next, using these models potentially low-performing students will be selected for interventions. Finally, data related to the performance of particular interventions and the employed classification models will be collected at the end of the semester. The proposed FSS framework is evaluated on two different real-world datasets that were obtained during two different semesters for two Computer Science courses at University of Houston, Texas. Our experimental results reveal that Multi-Layer Neural Networks performed the best, and the proposed modelling approach can efficiently identify students at risk, and recommend interventions to enhance their performance before the final exam of the semester. The evaluation of different classifiers on educational datasets gave some insights into how different data mining algorithms predict student performance and enhance student retention. Moreover, the experiments created valuable data about the performance of different interventions.