Robust kernel extreme learning machines for postgraduate learning performance prediction.

In the context of graduate learning in China, mentors are the teachers with the highest frequency of contact and the closest relationships with postgraduate students. Nevertheless, a number of issues pertaining to the relationship between mentors and postgraduate students have emerged with increasing frequency in recent years, resulting in a notable decline in the quality of graduate education. In this paper, we investigate the influence of the relationship between mentors and postgraduate students on the postgraduate learning performance, with postgraduate students' admission motivation and learning pressure acting as moderating variables. In practice, outliers often appear during the data collection stage, and they have a significant impact on the convergence speed and prediction accuracy of machine learning models. In order to mitigate the impact of outliers, we propose a novel kernel extreme learning machine model that is robust to outliers and name it a robust kernel extreme learning machine (RK-ELM). The RK-ELM model can automatically detect any data that may be corrupted by uncertain disturbances, thereby enhancing the robustness and generalization ability of the model. We take 873 full-time postgraduate students from universities in Zhejiang Province, China as the research object, and then form a dataset based on the postgraduates' questionnaire results and their grade point averages in the current academic year. Experimental results show that: 1) RK-ELM is an effective model for predicting postgraduate learning performance; 2) The relationship between mentors and postgraduates has a significant impact on learning performance, but it cannot directly predict learning performance; 3) The combination of the relationship between mentors and postgraduates and enrollment motivation can be used to predict learning performance, where the former can predict learning performance by influencing learning pressure.