In this study, the success of 6,054 college students "screened" as twice-exceptional (2e; i.e., those with significantly discrepant math vs. reading scores on the ACT [formerly, American College Test] or SAT [formerly, Scholastic Aptitude Test]) was examined based on major selection and type of potential learning disability using a…

Research has shown that males often struggle with the demands of college, one of which involves comprehending academic texts. Many males, however, do understand course readings and are successful students. Unfortunately, little is known about how successful and struggling college males differ in their use of "reading strategies," the…

This study presents a systematic review of the literature on the predicting student retention in higher education through machine learning algorithms based on measures such as dropout risk, attrition risk, and completion risk. A systematic review methodology was employed comprised of review protocol, requirements for study selection, and analysis…

This study uses data from the Integrated Postsecondary Education Data System and Delta Cost Project to identify institutional predictors of bachelor's degree completion rates for Pell Grant recipients and nonrecipients at public and private not-for-profit 4-year institutions. Descriptive analyses show that Pell recipients are relatively…

Student success measurements for 4-year institutions of higher education are a topic of importance for numerous stakeholders including prospective and current students, parents, staff, faculty, administrators, governing boards, policymakers, and citizens. Common measures of student success are retention rates and 4- and 6-year graduation rates.…

In this study, we identified factors influencing completion rates (graduation) of undergraduate students in science, technology, engineering, and mathematics (STEM). We used hierarchical, logistic, and linear regression models to analyze the data and found significant predictor variable interactions across various underrepresented versus…

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…

Science, Technology, Engineering, and Mathematics student success is an important topic in higher education research. Recently, the use of data analytics in higher education administration has gain popularity. However, very few studies have examined how data analytics may influence Science, Technology, Engineering, and Mathematics student success.…

Hispanic students are the most likely out of all racial or ethnic groups to be first-generation college students (FGCS). Hispanic FGCS have been shown to be the least likely to persist out of all racial or ethnic backgrounds. However, there is little literature on this population. To address this, the present study investigated the association of…

Students are most likely to leave science, technology, engineering, and mathematics (STEM) majors during their first year of college. We developed an analytic approach using random forests to identify at-risk students. This method is deployable midway through the first semester and accounts for academic preparation, early engagement in university…

First-generation (FG) students are generally less likely than their continuing-generation (CG) peers to persist and complete a degree. Using student data available at initial enrollment, this multi-institutional study examines retention and transfer at the second year in relation to academic readiness, financial resources, college intentions,…

Underrepresented college students are more likely than majority students to underperform and drop out of college. Perceptions of status relative to other students may play a key role in this process. The current study uses subjective social status (SSS) to predict underrepresented students' grade point average via their sense of academic…

Retention research rarely differentiates between students dismissed from an institution for poor academic performance versus students leaving by choice. As a proxy for studying academic dismissal, this study investigated differences between students leaving college in academic jeopardy after the first year (<2.00 grade point average) and those…

Student retention has garnered increased attentions in higher education. Drawing from various theoretical perspectives, researchers have developed multiple theoretical models to explain or predict student retention. Most models, however, were intended for traditional aged, full-time students at 4-year colleges or universities. The rapid growth of…

Two approaches, Linear Discriminant Analysis, and Logistic Regression are used and compared to predict success or failure for first-time freshmen in the first calculus course at a medium-sized public, 4-year institution prior to Fall registration. The predictor variables are high school GPA, the number, and GPA's of college prep mathematics…