The importance of retention rate for higher education institutions has encouraged data
analysts to present various methods to predict at-risk students. Their objective is to provide …

Each year, roughly 30% of first-year students at US baccalaureate institutions do not return
for their second year and over $9 billion is spent educating these students. Yet, little …

While predictive analytics have been used in a wide range of situations, by far the most
common use of predictive analytics is to improve student retention and academic …

Predicting student retention is a crucial task for all stakeholders in higher education. This
paper surveyed the Educational Data Mining (EDM) literature to explore the most recent …

Graduation and dropout rates have always been a serious consideration for educational
institutions and students. High dropout rates negatively impact both the lives of individual …

In higher education, predictive analytics can provide actionable insights to diverse
stakeholders such as administrators, instructors, and students. Separate feature sets are …

Recent reports indicate that 40 percent of freshman at four-year public colleges will not
graduate. Further, the average completion rate for two-year community colleges is less than …

Analytical models developed in offline settings with pre-prepared data are typically used to
predict students' performance. However, when data are available over time, this learning …

Student retention is an essential measurement metric in education, indicated by retention
rates, which are accumulated as students re-enroll from one academic year to the next. High …

The presence of" big data" in higher education has led to the increasing popularity of
predictive analytics for guiding various stakeholders on appropriate actions to support …