id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
1v53jw8435h	Everaldo Aguiar	Identifying Students at Risk and Beyond: A Machine Learning Approach	1904		.txt	text/plain	241	8	41	To stem the tide of student attrition, these institutions continue to invest increasing amounts of resources to foster the early identification of students who may drop out. We compare the performance of such methods to that of more traditional approaches, discuss what specific student-level data features can be used to measure student risk, and delineate strategies that can be followed by academic institutions that wish to deploy and evaluate these predictive models.	cache/1v53jw8435h.txt	txt/1v53jw8435h.txt