A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes

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Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, methodology): “Second dataset consists of student performance records for about 21K students who were set to graduate in 2012 and 2013 from schools in a medium-sized district on the east coast [23].”
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables (2017, background): “ere has been a lot of interest in developing accurate risk assessment models in health care [6], education [19], and criminal justice [7].”
How to Design AI for Social Good: Seven Essential Factors (2020, methodology): “For example, researchers have used machine learning to predict academic adversity (Lakkaraju et al. 2015).”
Approximate and Exact Enumeration of Rule Models (2018, background): “They have a wide range of applications, in ﬁelds such as medical diagnosis (Ong, Wang, and Mu 2014), judicial decisions (Jordan and Freiburger 2015), and education (Lakkaraju et al. 2015), to name a few.”
A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques (2021, background): “Lakkaraju et al. [38] proposed a machine learning framework for identifying the student at risk of failing to graduate or students at risk of not graduating on time.”