Graduate Employment Prediction with Bias

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

Graduate employment prediction with bias

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. Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record*

Readings on L2 reading: Publications in other venues 2015–2016

This feature offers an archive of articles published in other venues during the past year and serves as a valuable tool to readers of Reading in a Foreign Language (RFL). It treats any topic within the scope of RFL and second language reading. The articles are listed in alphabetical order, each with a complete reference as well as a brief summary. The editors of this feature attempt to include all related articles that appear in other venues. However, undoubtedly, this list is not exhaustive

2006-2007 Lindenwood University Undergraduate Course Catalog

Lindenwood University Undergraduate Course Cataloghttps://digitalcommons.lindenwood.edu/catalogs/1114/thumbnail.jp

2007-2008 Lindenwood University Undergraduate Course Catalog

Lindenwood University Undergraduate Course Cataloghttps://digitalcommons.lindenwood.edu/catalogs/1149/thumbnail.jp

2005-2006 Lindenwood University Undergraduate Course Catalog

Lindenwood University Undergraduate Course Cataloghttps://digitalcommons.lindenwood.edu/catalogs/1111/thumbnail.jp

2008-2009 Lindenwood University Undergraduate Course Catalog

Lindenwood University Undergraduate Course Cataloghttps://digitalcommons.lindenwood.edu/catalogs/1151/thumbnail.jp