Gao Hongxing, Xu Tianzi, Zhang Nan
Faculty of Education, Shaanxi Normal University, Xi'an, 710062, China.
Graduate School, Wenzhou University, Wenzhou, 325035, China.
Heliyon. 2024 Dec 9;11(1):e40919. doi: 10.1016/j.heliyon.2024.e40919. eCollection 2025 Jan 15.
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.
在中国研究生学习的背景下，导师是与研究生接触频率最高、关系最密切的教师。然而，近年来，导师与研究生关系中出现了一些问题，且频率不断增加，导致研究生教育质量显著下降。在本文中，我们以研究生的入学动机和学习压力作为调节变量，研究导师与研究生关系对研究生学习成绩的影响。在实践中，数据收集阶段经常会出现异常值，它们对机器学习模型的收敛速度和预测精度有重大影响。为了减轻异常值的影响，我们提出了一种对异常值具有鲁棒性的新型核极限学习机模型，并将其命名为鲁棒核极限学习机（RK-ELM）。RK-ELM模型可以自动检测任何可能被不确定干扰破坏的数据，从而提高模型的鲁棒性和泛化能力。我们以中国浙江省高校的873名全日制研究生为研究对象，然后根据研究生的问卷调查结果及其本学年的平均绩点形成一个数据集。实验结果表明：1）RK-ELM是预测研究生学习成绩的有效模型；2）导师与研究生之间的关系对学习成绩有显著影响，但不能直接预测学习成绩；3）导师与研究生之间的关系和入学动机的组合可用于预测学习成绩，其中前者可通过影响学习压力来预测学习成绩。
