Recent advances in genomics make it possible to predict individual differences in education from polygenic scores that are person-specific aggregates of inherited DNA differences. Here, we systematically reviewed and meta-analyzed the strength of these DNA-based predictions for educational attainment (e.g., years spent in full-time education) and…

Measures of student disadvantage--or risk--are critical components of equity-focused education policies. However, the risk measures used in contemporary policies have significant limitations, and despite continued advances in data infrastructure and analytic capacity, there has been little innovation in these measures for decades. We develop a new…

Study success in Higher Education is of primary importance in the European policy agenda. Yet, given the diverse educational landscape across countries and institutions, more coordinated action is needed to gain a more solid knowledge of the dropout phenomenon. This study aims to gain a better insight into students' dropout based on an integrated…

This article provides evidence about predictive relationships between features of community college interventions and their impacts on student progress. This evidence is based on analyses of student-level data from large-scale randomized trials of 39 (mostly) community college interventions. Specifically, the evidence consistently indicates that…

Can genetic screening be used to personalize education for students? Genome-wide association studies (GWAS) screen an individual's DNA for specific variations in their genome, and how said variations relate to specific traits. The variations can then be assigned a corresponding weight and summed to produce polygenic scores (PGS) for given traits.…

This article provides evidence about predictive relationships between features of community college interventions and their impacts on student progress. This evidence is based on analyses of student-level data from large-scale randomized trials of 39 (mostly) community college interventions. Specifically, the evidence consistently indicates that…

This article uses an anonymous 2014-15 school year dataset from the Directorate-General for Statistics of Education and Science (DGEEC) of the Portuguese Ministry of Education as a means to carry out a predictive power comparison between the classic multilinear regression model and a chosen set of machine learning algorithms. A multilinear…

NWEA claimed their assessment results could accurately predict student performance in reading and mathematics on the ACT Aspire for students in Grades 7 through 10. The purpose of this study was to determine the relationship between students' scores on the NWEA MAP tests in reading and mathematics and the ACT Aspire for students in Grades 7…

Considerable attention has been given to methods for knowledge estimation, a category of methods for automatic assessment of a student's degree of skill mastery or knowledge at a specific time. Knowledge estimation is frequently used to make decisions about when a student has reached mastery and is ready to advance to new material, but there has…

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…

Among community college leaders and others interested in reforms to improve student success, there is growing interest in adopting machine learning (ML) techniques to predict credential completion. However, ML algorithms are often complex and are not readily accessible to practitioners for whom a simpler set of near-term measures may serve as…

Predicting the students' performance has become a challenging task due to the increasing amount of data in educational systems. In keeping with this, identifying the factors affecting the students' performance in higher education, especially by using predictive data mining techniques, is still in short supply. This field of research is usually…

Objective: This study examines how accurately a small set of short-term academic indicators can approximate long-term outcomes of community college students so that decision-makers can take informed actions based on those indicators to evaluate the current progress of large-scale reform efforts on long-term outcomes, which in practice will not be…

The purpose of this research was to examine the relationship between State of Texas Assessments of Academic Readiness (STAAR) reading and academic progress in the programs of Istation and Star Renaissance (Star Ren) in fourth grade special education students. Special education students in the selected school district appear to produce inconsistent…

A predictive model for the USMLE Step 1 was created based on NBME Comprehensive Basic Sciences Self-Assessment (CBSSA) exams. All second year medical students at a southwestern university from 2014-2016 took a NBME CBSSA exam under controlled testing conditions six months (January) and three months (April) prior to their Step 1. A multiple…