This study examined the predictive power of students' demographic characteristics, reading attitudes, school characteristics, and teacher-informed reading activities on three metacognitive reading skills: understanding and remembering, summarizing, and assessing credibility and their influence on 15-year-old students' reading scores. The dataset…

This study attempts to predict secondary school students' performance in English and Mathematics subjects using data mining (DM) techniques. It aims to provide insights into predictors of students' performance in English and Mathematics, characteristics of students with different levels of performance, the most effective DM technique for students'…

The purpose of this brief is to provide school-level examples of how early college practitioners are collecting and using data to improve their practices. Examples three and four are school-level data from two early college partnerships: the MetroWest CPC (Framingham, Milford, Waltham), and Lawrence. The brief begins, however, with the national…

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…

The COVID-19 pandemic forced widespread school closures and a shift to remote learning. A growing body of research has examined the effects of remote learning on student outcomes. But the accuracy of the school modality measures used in these studies is questionable. The most common measures--based on self-reports or district website…

The purpose of this study is to examine the effect of missing data imputation methods, namely regression imputation (RI), multiple imputation (MI) and k-nearest neighbor (kNN) on differential item functioning (DIF). In this regard, the datasets used in the research were created by deleting some of the data via the missing completely at random…

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…

The research aims to determine the factors affecting PISA 2018 reading skills using the Random Forest and MARS methods and to compare their prediction abilities. This study used the information from 5713 students, 2838 (49.7%) male and 2875 (50.3%) female, in the PISA 2018 Turkey. The analysis shows the MARS method performed better than the Random…

Accountability has pushed principals to use data to drive and inform decisions in schools to positively impact student achievement. Research has shown that principals are the second most important impact on student achievement, second only to teachers. Principals who can lead change in schools based on data driven response have a positive impact…

We examined the impact of secondary Career and Technical Education (CTE) credit-taking on students attaining full-time employment following graduation from high school in the United States, and whether such credit-taking impacted students entering employment in a related field. We focused on occupational programs, and we used four levels of CTE…

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…

The EdSurvey package helps users download, explore variables in, extract data from, and run analyses on large-scale assessment data. The analysis functions in EdSurvey account for the use of plausible values for test scores, survey sampling weights, and their associated variance estimator. We describe the capabilities of the package in the context…

In tiered instructional systems (Response to Intervention [RTI]/Multitier System of Supports [MTSS]) that rely on ongoing assessment of students at risk of experiencing academic difficulties, the ability to make informed decisions using student data is critical for student learning. Prior research has demonstrated that, on average, teachers have…

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,…

Regional comprehensive universities offer accessible and diverse undergraduate educational programs, while grappling with funding cuts and affordability. The study's first research question underscores the enduring importance of factors such as student characteristics, pre-college characteristics, and financial situations. The findings highlight…