Purpose: This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India.…

This paper examines performance differences among demographic groups on the ETS Major Field Test in Business. The study employs the Blinder-Oaxaca decomposition technique to analyze the test score differentials by gender and racial minority status. This technique decomposes the difference into two parts: an endowment effect (or explained portion)…

A comparative longitudinal data analysis between two online non-thesis master's programs--natural resource management and environmental science--in a college of natural resources to determine the relationship between student characteristics and disenrollment risks. Risks varied between the two programs, with significance found to increase the risk…

Recent insights have underscored the role of context in cultivating intelligence mindsets' influence on students' academic outcomes. Psychological affordances of the social context may encourage an adaptive perspective (i.e., growth mindset). Expanding this novel area of investigation, we examined how students' sense of belonging, as an affordance…

I study student characteristics and academic performance at the University of California, where consideration of an applicant's ethnicity has been banned since 1996 and SAT scores were used in admitting students to the university until fall 2021. I show the following: (1) SAT scores were more important than high school grades in predicting…

The purpose of this non-experimental, quantitative, comparative study was to compare academic outcomes (final GPA, retention, graduation rates) and student engagement measures of students who enroll in an honors program at a Tennessee community college versus those who were honors-eligible but did not participate in an honors program. Findings…

Wellness, persistence, and retention in higher education have a common goal of student success. Colleges and universities explore various methods to help students succeed and continue towards degree completion. Most of the research has focused on traditional predictors (e.g., high school GPA, ACT/SAT scores) of persistence and success. There is…

A significant amount of research has gone into predicting student performance and many studies have been conducted to predict why students drop out. A variety of data including digital footprints, socio-economic data, financial data, and psychological aspects have been used to predict student performance at the test, course, or program level.…

Even with extensive retention research dating from the 1960s, community colleges still struggle to identify the reasons why students do not return to college. Data mining has allowed these retention models to evolve to identify new patterns among student populations and variables. The purpose of this study was to create a predictive model for…

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…

Increasing productivity in educational systems is of great importance. Researchers are keen to predict the academic performance of students; this is done to enhance the overall productivity of educational system by effectively identifying students whose performance is below average. This universal concern has been combined with data science…

Currently, the state of California has dedicated much focus to reducing absenteeism in schools through the In School + On Track initiative, which revitalizes efforts made to keep accurate and informative attendance data. Additionally, absenteeism has been integrated into California's Local Control and Accountability Plan to monitor district…

The use of machine learning and data mining techniques across many disciplines has exploded in recent years with the field of educational data mining growing significantly in the past 15 years. In this study, random forest and logistic regression models were used to construct early warning models of student success in introductory calculus-based…

Higher education leaders have repeatedly called for improved diversity, equity, and inclusion efforts, but many institutions continue to fall short. Data can play an integral role in this work; key among them are data on student demographics, including race/ethnicity. Meeting diversity, equity, and inclusion goals requires a thorough and nuanced…

This descriptive and correlational research studies 15,658 students from 335 secondary schools in the state of Chihuahua, Mexico, through the results of the examination of admission to high school education (National High School Admission Test--EXANI I from the National Assessment Center for Education--CENEVAL) on logical-mathematical and verbal…