SUMMER: Bias-aware Prediction of Graduate Employment Based on Educational Big Data

Xia, Feng, Guo, Teng, Bai, Xiaomei, Shatte, Adrian, Liu, Zitao, and Tang, Jiliang (2022) SUMMER: Bias-aware Prediction of Graduate Employment Based on Educational Big Data. ACM/IMS Transactions on Data Science, 2 (4). 39.

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The failure of obtaining employment could lead to serious psychosocial outcomes such as depression and substance abuse, especially for college students who may be less cognitively and emotionally mature. In addition to academic performance, employers’ 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 (biaS-aware gradUate eMployMEnt pRediction) to predict students’ 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 Wasserstein generative adversarial network with gradient penalty (WGAN-GP) 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.

Item ID: 81629
Item Type: Article (Research - C1)
ISSN: 2577-3224
Keywords: Graduate employment,;prediction;bias;educational big data;data analysis
Copyright Information: © 2022 Association for Computing Machinery.
Date Deposited: 31 Jan 2024 00:07
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460105 Applications in social sciences and education @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 50%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 100%
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