We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most…

Individuals with autism increasingly enroll in universities, but little is known about predictors for their success. This study developed predictive models for the academic success of autistic bachelor students (N = 101) in comparison to students with other health conditions (N = 2465) and students with no health conditions (N = 25,077). We…

Purpose: The emerging field of educational data mining provides an opportunity to process large-scale data emerging from higher education institutions (HEIs) into reliable knowledge. The purpose of this paper is to examine factors influencing persistence amongst students enrolled in a Chemistry major at a South African university using enrolment…

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 timely information that may enable educators to channel the most effective remedial treatments towards precisely targeted students in an efficient manner. The present…

We perform two approaches of machine learning, logistic regressions and decision trees, to predict student dropout at the Karlsruhe Institute of Technology (KIT). The models are computed on the basis of examination data, i.e. data available at all universities without the need of specific collection. Therefore, we propose a methodical approach…

The Hispanic population in the United States has been increasing, which is affecting the number of Hispanic student population in the higher education. In spite of the rapid increase of Hispanic student population, little empirical research has been conducted on the Hispanic student's college success. This study investigated the effect of…

This study assessed the feasibility of using precollege success indicators to identify at-risk students at a large 4-year public research university in the Midwest. Retention data from students who participated in an established student success program were examined. The findings affirm that the initial admissions assessment identifying at-risk…

This study is an exploration of the probability of modeling higher education to optimize student retention for a desired academic outcome. As college students can be viewed as "consumers" of education institutions, this paper examines the applicability of business concepts of customer loyalty and retention and reviews the business…

Increasing student retention (successfully finishing a particular course) and persistence (continuing through a sequence of courses or the major area of study) is currently a major challenge for universities. While students' academic and social integration into an institution seems to be vital for student retention, research into the effect of…

The purpose of this study was to use a comprehensive framework to examine academic, psychosocial, noncognitive, and other background factors that are related to retention at a large, public four-year institution in the southeastern United States. Specifically, the study examined what factors are most important in predicting first-to-second year…

Farrington and colleagues developed a model that contends that academic mindsets, academic perseverance, learning strategies, social skills, and academic behaviors affect academic success. This study tests a modified version of this model with first-year students (n = 1,603) at a large, ethnically diverse, urban university. The hypothesized…

In the 21st century, attainment of a college degree is more important than ever to achieve economic self-sufficiency, employment, and an adequate standard of living. Projections suggest that by 2020, 65% of jobs available in the U.S. will require postsecondary education. This reality creates an unprecedented demand for higher education, and…

During the last decade, there have been numerous reports detailing the importance of increasing science, technology, engineering, and math (STEM) majors in the United States. Simultaneously, an increasing number of studies are being developed to predict a student's success and completion of a STEM degree, recognizing that retention is a…

The metrics used in the US to track students do not include adults and part-time students. This has led to the development of a massive data initiative--the Predictive Analytics Reporting (PAR) framework--that uses predictive analytics to trace the progress of all types of students in the system. This development has allowed actionable,…

Retention rates of first generation college student (FGCS) became a growing concern for leaders of educational institutions in the U.S. FGCS were several times more likely to exit college without a degree as compared to their traditional counterparts. The Model of Institutional Departure offered a general framework of college student retention.…