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…

Game-based learning (GBL) is increasingly recognized as an effective tool for teaching diverse skills, particularly in science education, due to its interactive, engaging, and motivational qualities, along with timely assessments and intelligent feedback. However, more empirical studies are needed to facilitate its wider application in school…

Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th,…

Background: Most United States schools include school-based physical fitness testing (SB-PFT), yet little evidence shows how it is implemented, perceived, and used. Purpose: 1) Explore stakeholders' experiences with SB-PFT; 2) identify: challenges in analyzing fitness data, ways schools can use fitness data, and predictors of meeting fitness…

To support student success effectively, school teams need information on known predictors of youth behavior and academic performance. In contrast to measures of behavioral and academic outcomes that are commonly relied on in schools, the School Success Profile (SSP) for middle and high school students provides comprehensive information on…

Background: Computational thinking is derived from arguments that the underlying practices in computer science augment problem-solving. Most studies investigated computational thinking development as a function of learners' factors, instructional strategies and learning environment. However, the influence of the wider community such as educational…

Examining students' attitudes towards STEM (science, technology, engineering, and mathematics) fields starting from middle school level is important in their career choices and future planning. However, there is a need to investigate which variables affect students' attitudes towards STEM. Here, we aimed to estimate middle school students'…

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…

Education systems produce a large number of valuable data for all stakeholders. The processing of these educational data and making studies on the future of education based on the data reveal highly meaningful results. In this study, an insight was tried to be developed on the educational data collected from ninth-grade students by using data…

The purpose of the study is to analyze the affective traits that affect mathematics achievement through Structural Equation Modeling (SEM) as a traditional regression model and Multivariate Adaptive Regression Splines (MARS), as one of the data mining methods. Structural Equation Modeling, one of the regression-based methods, is quite popular for…

Although educators frequently use assessment to identify who needs supplemental instruction and if that instruction is working, there is a lack of research investigating assessment that informs what instruction students need. The purpose of the current study was to determine if a brief (approximately 20 min) task that reflects a common middle…

Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research…

This report describes how Connecticut took steps to collect consistent attendance data by learning mode -- remote, in-person and hybrid -- and publicly released data in a timely manner during the pandemic. For example, the Connecticut State Department of Education (CSDE) agreed upon a standard definition of attendance -- showing up to school for…

The well-studied Baker et al., affect detectors on boredom, frustration, confusion and engagement concentration with ASSISTments dataset were used to predict state tests scores, college enrollment, and even whether a student majored in a STEM field. In this paper, we present three attempts to improve upon current affect detectors. The first…

Background: We examined prevalence, incidence, and trajectory of obesity from kindergarten through grade 8 in one of the first states to implement annual surveillance. Methods: Participants included 16,414 children enrolled in kindergarten in Arkansas in 2004 with complete body mass index (BMI) measurements in kindergarten and eighth grade.…