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In addition to academic performance, employers\u2019 unconscious biases are a potential obstacle to graduating students in becoming employed. Thus, it is necessary to understand the nature of such unconscious biases to assist students at an early stage with personalized intervention. In this paper, we analyze the existing bias in college graduate employment through a large-scale education dataset and develop a framework called SUMMER (bia\n                    <jats:bold>S<\/jats:bold>\n                    -aware grad\n                    <jats:bold>U<\/jats:bold>\n                    ate e\n                    <jats:bold>M<\/jats:bold>\n                    ploy\n                    <jats:bold>ME<\/jats:bold>\n                    nt p\n                    <jats:bold>R<\/jats:bold>\n                    ediction) to predict students\u2019 employment status and employment preference while considering biases. The framework consists of four major components. Firstly, we resolve the heterogeneity of student courses by embedding academic performance into a unified space. Next, we apply a\n                    <jats:bold>Wasserstein generative adversarial network with gradient penalty (WGAN-GP)<\/jats:bold>\n                    to overcome the label imbalance problem of employment data. Thirdly, we adopt a temporal convolutional network to comprehensively capture sequential information of academic performance across semesters. Finally, we design a bias-based regularization to smooth the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework.\n                  <\/jats:p>","DOI":"10.1145\/3510361","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T12:55:23Z","timestamp":1643720123000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["SUMMER: Bias-aware Prediction of Graduate Employment Based on Educational Big Data"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8324-1859","authenticated-orcid":false,"given":"Feng","family":"Xia","sequence":"first","affiliation":[{"name":"Federation University Australia, Australia and Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6604-475X","authenticated-orcid":false,"given":"Teng","family":"Guo","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9826-4802","authenticated-orcid":false,"given":"Xiaomei","family":"Bai","sequence":"additional","affiliation":[{"name":"Anshan Normal University, Anshan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6225-9697","authenticated-orcid":false,"given":"Adrian","family":"Shatte","sequence":"additional","affiliation":[{"name":"Federation University Australia, Ballarat, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0491-307X","authenticated-orcid":false,"given":"Zitao","family":"Liu","sequence":"additional","affiliation":[{"name":"TAL Education Group, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7125-3898","authenticated-orcid":false,"given":"Jiliang","family":"Tang","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,3,30]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-018-0496-z"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1257\/jep.35.3.3"},{"key":"e_1_3_1_4_2","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai Shaojie","year":"2018","unstructured":"Shaojie Bai, J. 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