A robust random forest-based tri-training algorithm for early in-trouble student prediction

Educational data mining has received much attention worldwide due to its significance in the education domain. Among a large number of the educational data mining tasks, early in-trouble student prediction is a popular one. This task focuses on identifying the students who are at risk in their study as soon as possible before the end of the permitted period of study time. For early detection, data shortage is a challenge for the task at both instance and set levels. Indeed, at the instance level, incomplete data could be gathered for each student at his/her early study period and also at the set level, many labeled data could not be collected for their final study status. Therefore, a solution to the task in such a context is required. In this paper, we propose a robust random forest-based Tri-training algorithm that can overcome that data shortage challenge. In particular, based on the semi-supervised learning process of the original Tri-training algorithm, an incomplete data handling method is integrated into its iterative mechanism so that the Tri-training algorithm can be more robust. In addition, a new combination of the Tri-training algorithm and a random forest model is examined so that each classifier of the Tri-training model can be enhanced for more accurate predictions. As a result, the proposed algorithm is an effective solution to the early in-trouble student prediction task. Its effectiveness has been confirmed with the better experimental results on real data sets in comparison with the existing methods using the preprocessing approach.