Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…

In this book, best-selling author Susan M. Brookhart helps teachers and administrators understand the critical elements and nuances of assessment data and how that information can best be used to inform improvement efforts in the school or district. Readers will learn: (1) What different kinds of data can--and cannot--tell us about student…

When good model-data fit is observed, the Many-Facet Rasch (MFR) model acts as a linking and equating model that can be used to estimate student achievement, item difficulties, and rater severity on the same linear continuum. Given sufficient connectivity among the facets, the MFR model provides estimates of student achievement that are equated to…

In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…

The purpose of this study is to provide an account of teacher perception of core practice tasks in data use, particularly data interpretation. Data interpretation is critical to professional practice in planning instructional adjustments for student learning. This is a case study of four elementary teachers who provide numerous task-specific…

This dissertation comprises three papers that propose, discuss, and illustrate models to make improved inferences about research questions regarding student achievement in education. Addressing the types of questions common in educational research today requires three different "extensions" to traditional educational assessment: (1)…

This "Practitioner's Guide to Growth Models," commissioned by the Technical Issues in Large-Scale Assessment (TILSA) and Accountability Systems & Reporting (ASR), collaboratives of the "Council of Chief State School Officers," describes different ways to calculate student academic growth and to make judgments about the…

Background: Teacher-related factors such as gender, experience, conceptions related to mathematics, instructional practices have effects with various magnitudes on students' mathematics achievement. Classroom related factors such as class size, class climate and limitations to teaching and their relation to mathematics achievement have also been…

In the Philadelphia Public Schools, a computer system called Data to Success has been created to assess student progress prior to end-of-year testing. The five-stage system allows a school to influence teacher decisions, identify student warning signs, and monitor important trend and analysis information throughout the school year. (MLH)

Article reanalyzes and expands upon data from Tennessee's Project STAR which examined the effects of class size reduction on student achievement in the primary grades. It describes six competing theories of class size impact on achievement and test performance, settling on the student group/modeling interpretation of study data. (SM)