GRADE: Machine Learning Support for Graduate Admissions

By Austin Waters, Risto Miikkulainen. 2013

114 citations recorded by the source corpus.

This paper describes GRADE, a statistical machine learning system developed to support the work of the graduate admissions committee at the University of Texas at Austin Department of Computer Science (UTCS). In recent years, the number of applications to the UTCS PhD program has become too large to manage with a traditional review process. GRADE uses historical admissions data to predict how likely the committee is to admit each new applicant. It reports each prediction as a score similar to those used by human reviewers, and accompanies each by an explanation of what applicant features most influenced its prediction. GRADE makes the review process more efficient by enabling reviewers to spend most of their time on applicants near the decision boundary and by focusing their attention on parts of each applicant’s file that matter the most. An evaluation over two seasons of PhD admissions indicates that the system leads to dramatic time savings, reducing the total time spent on reviews by at least 74%.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning (2018, background): “, admitting a student to university [101], predicting if a defendant awaiting trial can be released safely [102], modeling consumer credit risk [103].”
Fair Regression: Quantitative Definitions and Reduction-based Algorithms (2019, background): “Predictions of these quantities are treated as scores, which are used by human decision makers, perhaps in the context of a partly automated workﬂow, to reach ﬁnal decisions (see, e.g., Waters & Miikkulainen, 2014; US Federal Reserve, 2007; Northpointe, 2010; Lowenkamp et al., 2012).”
Explaining machine learning classifiers through diverse counterfactual explanations (2019, background): “Similar to the loan example, this argument is valid for a range of scenarios involving decision-making on an individual’s outcome, such as deciding admission to a university [32], screening job applicants [27], disbursing government aid [2, 4], and identifying people at high risk of a future disease”
Algorithmic Bias in Education (2021, background): “…practices at scale for a range of applications, often high-stakes, including dropout prediction (Christie et al., 2019; Milliron et al., 2014), automated essay scoring (Ramineni & Williamson, 2013), graduate admissions (Waters & Miikkulainen, 2014), and knowledge inference (Ritter et al., 2016).”
The explanation game: a formal framework for interpretable machine learning (2020, unknown): “ML has been used to help evaluate loan applications (Munkhdalai et al. 2019) and student admissions (Waters and Miikkulainen 2014), predict criminal recidivism (Dressel and Farid 2018), and identify military targets (Nasrabadi 2014), to name just a few controversial examples.”