The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students' employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework.

本研究提出了MAYA预测框架，该框架通过将学术表现嵌入到统一空间中，采用生成对抗网络（GAN）解决了课程异质性问题，采用带有新型dropout机制的LSTM综合捕捉学期间的顺序信息，并设计基于偏见的正则化来捕捉就业市场的偏见。 在大规模教育数据集上进行了广泛的实验，结果证明了此预测框架的有效性。

毕业生就业预测及其偏差