id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_z3zntoflhnd7phrocdyphm5at4	Ashutosh Nandeshwar	Learning patterns of university student retention	2011	28	.pdf	application/pdf	13527	4171	86	Keywords: data mining, student retention, predictive modeling, financial aid This article uses data mining to find patterns of student retention at American Universities. This article applies data mining methods to the problem of studying student retention. • Previous data mining studies on these student records can be greatly improved using discretization, attribute selection and cross-validation over various algorithms. Druzdzel & Glymour (1994) were among the first researchers to apply knowledge discovery algorithm to study the student retention problem. college ranking data to find the factors that influenced student retention, and they found and decisions trees to survey data at the University of Belgium to classify new students logistic regression, Bayesian Classifiers, and decision trees) applied to the student retention problem, and also used attribute evaluators to generate rankings of important For example, Herzog studied a data set with a 83.5% retention rate (see Table 1).	./cache/work_z3zntoflhnd7phrocdyphm5at4.pdf	./txt/work_z3zntoflhnd7phrocdyphm5at4.txt