Maintaining and cultivating student engagement is critical for learning. Understanding factors affecting student engagement can help in designing better courses and improving student retention. The large number of participants in massive open online courses (MOOCs) and data collected from their interactions on the MOOC open up avenues for studying…

Educational institutions increasingly recognize the role that student belonging plays in retention. Many studies in this area focus on helping students improve a sense of belonging before they matriculate or identifying belonging as a reason for their departure. This study measures students' sense of belonging at key transition points during the…

Objective: This study examined the effects of offering proactive student-success coaching, informed by predictive analytics, on student academic performance and persistence. Specifically, this study investigated semester grade point average (GPA) and semester-to-semester persistence of community college students as outcomes. Methods: This study…

Despite the potential of Predictive Learning Analytics (PLAs) to identify students at risk of failing their studies, research demonstrating effective application of PLAs to higher education is relatively limited. The aims of this study are: (1) to identify whether and how PLAs can inform the design of motivational interventions; and (2) to capture…

Purpose: The purpose of this case study was to determine the impact of utilizing predictive modeling to improve successful course completion rates for at-risk students at California community colleges. A secondary purpose of the study was to identify factors of predictive modeling that have the most importance for improving successful course…

The purpose of this study was to examine the effects of proactive student-success coaching, informed by predictive analytics, on student academic performance and persistence. Specifically, semester GPA and semester-to-semester student persistence were the investigated outcomes. Uniquely, the community college focused the intervention on only…

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.…

Many low-income young people are not reaching important milestones for success (for example, completing a program or graduating from school on time). But the social-service organizations and schools that serve them often struggle to identify who is at more or less risk. These institutions often either over- or underestimate risk, missing…

Online education continues to become an increasingly prominent part of higher education, but many students struggle in distance courses. For this reason, there has been considerable interest in predicting which students will succeed in online courses and which will receive poor grades or drop out prior to completion. Effective intervention depends…

Curricular changes that promote undergraduate persistence in science, technology, engineering, and mathematics (STEM) disciplines are likely associated with particular student psychological outcomes, and tools are needed that effectively assess these developments. Here, we describe the theoretical basis, psychometric properties, and predictive…

Many researchers have explored the relationships between the likelihood of graduating from college and demographic and pre-college factors such as gender, race/ethnicity, high school grade point average (GPA), and standardized test scores. However, additional factors such as a student's college major, home address, or use of learning support in…

The Motivation for Educational Attainment (MEA) questionnaire, developed to assess facets related to early adolescents' motivation to complete high school, has a bifactor structure with a large general factor and three smaller orthogonal specific factors (teacher expectations, peer aspirations, value of education). This prospective validity study…

Retention is a complex issue of great importance to community colleges. Several retention models have been developed to help explain this phenomenon. However, these models typically have used four-year college and university environments to build their foundations. Several researchers have attempted to identify predictor variables using…

The accountability of colleges and universities is a high priority for those making policy decisions. The purpose of this study was to determine institutional characteristics predicting retention rates, graduation rates and transfer-out rates using publicly available data from the US Department of Education. Using regression analysis, it was…

This study explored the interaction between student characteristics and the online environment in predicting course performance and subsequent college persistence among students in a large urban U.S. university system. Multilevel modeling, propensity score matching, and the KHB decomposition method were used. The most consistent pattern observed…