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…

Since Accumulated Grad-Point Average (AGPA) is crucial in the professional life of students, it is an interesting and challenging problem to create profiles for those students who are likely to graduate with low AGPA. Identifying this kind of students accurately will enable the university staff to help them improve their ability by providing them…

Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…

The graduate admissions process is crucial for controlling the quality of higher education, yet, rules-of-thumb and domain-specific experiences often dominate evidence-based approaches. The goal of the present study is to dissect the predictive power of undergraduate performance indicators and their aggregates. We analyze 81 variables in 171…

The study used earliest available student data from a flagship university in the southeast United States to build data mining models like logistic regression with different variable selection methods, decision trees, and neural networks to explore important student characteristics associated with retention leading to graduation. The decision tree…

This paper reports on a study to predict students at risk of failing based on data available prior to commencement of first year. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines, n=1,207. Data was gathered from both student enrollment data and an online, self-reporting,…

Conducting research and engaging in discussions with administrators and legislators can be important contributions toward alleviating the trend toward lower graduation rates among college students. This study used archival data obtained from the university registrar to examine how engagement in a credit-bearing undergraduate career course related…

In the context of "academic momentum," a longitudinal study of university students (N = 904) showed high school achievement and ongoing university achievement predicted subsequent achievement through university. However, the impact of high school achievement diminished, while additive effects of ongoing university achievement continued.…

A review of 13 years of research into antecedents of university students' grade point average (GPA) scores generated the following: a comprehensive, conceptual map of known correlates of tertiary GPA; assessment of the magnitude of average, weighted correlations with GPA; and tests of multivariate models of GPA correlates within and across…

Multilevel modeling provides several advantages over traditional ordinary least squares regression analysis; however, reporting results to stakeholders can be challenging. This article suggests some strategies for presenting complex, multilevel data and statistical results to institutional and higher education decision makers. The article is…

An extension of the methods of path analysis to include studies of categorical data was described and exemplified in a causal study of college dropouts. The usual models and methods of causal (path) analysis were designed for the study of quantitative variables and are not appropriate when the variables under investigation are categorical.…

A non-recursive model was developed to assess the degree of reciprocity between satisfaction and performance as indicated by grade point average. Data from students at a single research university were used. The causes of satisfaction differ for men and women. (Author/MLW)

The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…

Using data from a sample of 10 colleges at which most students had taken both SAT I: Reasoning Test and SAT II: Subject Tests, the authors simulated the effects of making selection decisions using SAT II scores in place of SAT I scores. Specifically, they treated the students in each college as forming the applicant pool for a more select college,…

Path analysis was used to explore the relationships among a number of variables related to student persistence. The subjects were freshmen in the College of Liberal Arts, University of Minnesota. Variables that showed the largest total effects on persistence were college grade-point average and high school rank. (Author/MLW)