Data from a moderately-selective state university in the Midwest is used to cross-examine the most appropriate data analytical techniques for predicting versus explaining college student persistence decisions. The current research provides an overview of the relative benefits of models specializing in prediction versus explanation with particular…

Recent methods have sought to improve precision in randomized controlled trials (RCTs) by utilizing data from large observational datasets for covariate adjustment. For example, consider an RCT aimed at evaluating a new algebra curriculum, in which a few dozen schools are randomly assigned to treatment (new curriculum) or control (standard…

This study highlights the role and importance of advanced, machine learning-driven predictive models in enhancing the accuracy and timeliness of identifying students at-risk of negative academic outcomes in data-driven Early Warning Systems (EWS). K-12 school districts have, at best, 13 years to prepare students for adulthood and success. They…

Higher education institutes experience lower success rates in online learning environments compared to traditional learning. Students' engagement within the learning management system (LMS) is one of the main factors affecting students' academic performance and retention. This quantitative correlational-predictive study examined if, and to what…

This study aimed to extend previous literature comparing continuous methods of data collection to estimation data, but this time implementing the data collection procedures within a group discrete trial teaching format with three individuals diagnosed with autism spectrum disorder. Group discrete trial teaching was conducted in a classroom setting…

In higher education, predictive analytics can provide actionable insights to diverse stakeholders such as administrators, instructors, and students. Separate feature sets are typically used for different prediction tasks, e.g., student activity logs for predicting in-course performance and registrar data for predicting long-term college success.…

This study evaluated the forecasting accuracy of trend estimation methods applied to time-series data from computer adaptive tests (CATs). Data were collected roughly once a month over the course of a school year. We evaluated the forecasting accuracy of two regression-based growth estimation methods (ordinary least squares and Theil-Sen). The…

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 paper reports on a study that adopted a systematic approach to school-level early warning. The study examined areas where research consistently shows schools commonly experience decline: (1) leadership stability; (2) talent management; (3) organizational culture; (4) financial operations; and (5) instructional programming. Rather than relying…

Knowledge tracing is a fundamental area of educational data modeling that aims at gaining a better understanding of the learning occurring in tutoring systems. Knowledge tracing models fit various parameters on observed student performance and are evaluated through several goodness of fit metrics. Fitted parameter values are of crucial interest in…

With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…

Recent reforms emphasize a shift in how students should learn and demonstrate knowledge of science. These reforms call for students to learn content knowledge using science and engineering practices, creating integrated science knowledge. While there is existing literature about the development of integrated science knowledge assessments, few…

The District of Columbia Public Schools (DCPS) has prioritized efforts to support students' social and emotional learning (SEL) competencies, such as perseverance and social awareness. To measure students' SEL competencies and the school experiences that promote SEL competencies (school climate), DCPS began administering annual surveys to…

Student learning objectives (SLOs) have become an increasingly popular tool for teacher evaluations as an alternative to Value-added Models (VAMs). However, the use of SLOs faces two major challenges. First, the target setting is mostly subjective and arbitrary. Second, there is little evidence on the reliability and validity of the tool. In this…

Over 30 states have adopted the Early Childhood Environmental Rating Scale-Revised (ECERS-R) as a component of their program quality assessment systems, but the use of ECERS-R on such a large scale has raised important questions about implementation. One of the most pressing question centers upon decisions users must make between two scoring…