Graduate Employment Prediction with Bias
- URL: http://arxiv.org/abs/1912.12012v1
- Date: Fri, 27 Dec 2019 07:30:28 GMT
- Title: Graduate Employment Prediction with Bias
- Authors: Teng Guo, Feng Xia, Shihao Zhen, Xiaomei Bai, Dongyu Zhang, Zitao Liu,
Jiliang Tang
- Abstract summary: Failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide.
We develop a framework, i.e., MAYA, to predict students' employment status while considering biases.
- Score: 44.38256197478875
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Pre-trained Language Models [78.69526166193236]
Pre-trained Language models (PLMs) have been acknowledged to contain harmful information, such as social biases.
We propose sc Social Bias Neurons to accurately pinpoint units (i.e., neurons) in a language model that can be attributed to undesirable behavior, such as social bias.
As measured by prior metrics from StereoSet, our model achieves a higher degree of fairness while maintaining language modeling ability with low cost.
arXiv Detail & Related papers (2024-06-14T15:41:06Z) - Beyond human subjectivity and error: a novel AI grading system [67.410870290301]
The grading of open-ended questions is a high-effort, high-impact task in education.
Recent breakthroughs in AI technology might facilitate such automation, but this has not been demonstrated at scale.
We introduce a novel automatic short answer grading (ASAG) system.
arXiv Detail & Related papers (2024-05-07T13:49:59Z) - Co-Supervised Learning: Improving Weak-to-Strong Generalization with
Hierarchical Mixture of Experts [81.37287967870589]
We propose to harness a diverse set of specialized teachers, instead of a single generalist one, that collectively supervises the strong student.
Our approach resembles the classical hierarchical mixture of experts, with two components tailored for co-supervision.
We validate the proposed method through visual recognition tasks on the OpenAI weak-to-strong benchmark and additional multi-domain datasets.
arXiv Detail & Related papers (2024-02-23T18:56:11Z) - Causal Triplet: An Open Challenge for Intervention-centric Causal
Representation Learning [98.78136504619539]
Causal Triplet is a causal representation learning benchmark featuring visually more complex scenes.
We show that models built with the knowledge of disentangled or object-centric representations significantly outperform their distributed counterparts.
arXiv Detail & Related papers (2023-01-12T17:43:38Z) - Plagiarism deterrence for introductory programming [11.612194979331179]
A class-wide statistical characterization can be clearly shared with students via an intuitive new p-value.
A pairwise, compression-based similarity detection algorithm captures relationships between assignments more accurately.
An unbiased scoring system aids students and the instructor in understanding true independence of effort.
arXiv Detail & Related papers (2022-06-06T18:47:25Z) - Who will dropout from university? Academic risk prediction based on
interpretable machine learning [0.0]
It predicts academic risk based on the LightGBM model and the interpretable machine learning method of Shapley value.
From the local perspective, the factors affecting academic risk vary from person to person.
arXiv Detail & Related papers (2021-12-02T09:43:31Z) - Prediction of Students performance with Artificial Neural Network using
Demographic Traits [2.7636476571082373]
The study aims to develop a system to predict student performance with Artificial Neutral Network.
The model was developed based on certain selected variables as the input.
It achieved an accuracy of over 92.3 percent, showing Artificial Neural Network potential effectiveness.
arXiv Detail & Related papers (2021-08-08T11:46:41Z) - Tri-Branch Convolutional Neural Networks for Top-$k$ Focused Academic
Performance Prediction [28.383922154797315]
Academic performance prediction aims to leverage student-related information to predict their future academic outcomes.
In this paper, we analyze students' daily behavior trajectories, which can be comprehensively tracked with campus smartcard records.
We propose a novel Tri-Branch CNN architecture, which is equipped with row-wise, column-wise, and depth-wise convolution and attention operations.
arXiv Detail & Related papers (2021-07-22T02:35:36Z) - Jointly Modeling Heterogeneous Student Behaviors and Interactions Among
Multiple Prediction Tasks [35.15654921278549]
Prediction tasks about students have practical significance for both student and college.
In this paper, we focus on modeling heterogeneous behaviors and making multiple predictions together.
We design three motivating behavior prediction tasks based on a real-world dataset collected from a college.
arXiv Detail & Related papers (2021-03-25T02:01:58Z) - Supercharging Imbalanced Data Learning With Energy-based Contrastive
Representation Transfer [72.5190560787569]
In computer vision, learning from long tailed datasets is a recurring theme, especially for natural image datasets.
Our proposal posits a meta-distributional scenario, where the data generating mechanism is invariant across the label-conditional feature distributions.
This allows us to leverage a causal data inflation procedure to enlarge the representation of minority classes.
arXiv Detail & Related papers (2020-11-25T00:13:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.