State education agencies are sitting on rich repositories of quantitative and qualitative assessment data. This document is designed to provide a conceptual framework and implementation guidance that can help agency leadership leverage and interrogate student performance data in systematic ways for reporting, outreach, and planning purposes. The…

Machine learning methods for predictive analytics have great potential for uncovering trends in educational data. However, simple linear models still appear to be most widely used, in part, because of their interpretability. This study aims to address the issues of interpretability of complex machine learning classifiers by conducting feature…

Trends in International Mathematics and Science Study (TIMSS) is an international comparative study designed to measure trends in mathematics and science achievement at grades 4 and 8, as well as to collect information about educational contexts (such as students' schools, teachers, and homes) that may be related to student achievement. TIMSS has…

Statistical reasoning is a crucial mathematical competency that students often lack. While there have been studies on the use of Android teaching materials in statistics learning, few have focused on statistical reasoning using comic media. This study aimed to develop mobile Android-based teaching materials called StatCom to enhance students'…

Background: The sudden growth in online instruction due to COVID-19 restrictions has given renewed urgency to questions about remote learning that have remained unresolved. Web-based assessment software provides instructors an array of options for varying testing parameters, but the pedagogical impacts of some of these variations has yet to be…

This paper reports on teachers developing their own data literacy and then acting as data coaches for colleagues in their schools. The 13 teachers from 7 schools in the study analysed standardised data using a data conversation protocol to identify students with significant mathematical misconceptions. They then took data-informed action with…

In this investigation, the extent to which differences were present in the mathematics achievement by the ethnicity/race of Grade 3 students in Texas were analyzed. Data obtained from the Texas Education Agency Public Education Information Management System for all Texas Grade 3 students who took the State of Texas Assessment of Academic Readiness…

In learning, errors are ubiquitous and inevitable. As these errors may signal otherwise latent cognitive processes, tutors--and students alike--can greatly benefit from the information they provide. In this paper, we introduce and evaluate the Systematic Error Tracing (SET) model that identifies the possible causes of systematically observed…

The chapters in this online volume comprise the TIMSS & PIRLS International Study Center's technical report of the methods and procedures used to develop, implement, and report the results of TIMSS 2019. There were various technical challenges because TIMSS 2019 was the initial phase of the transition to eTIMSS, with approximately half the…

Assessment can be powerful when teachers are able to analyse, interpret and use information in ways that enhance their teaching and programmes, and students' learning. A qualitative approach was used to investigate how teachers of Years 4-8 students analyse, interpret and use information gained from administration of the Progressive Achievement…

This study examines the use of data analytics to evaluate students' behaviours during their participation in an online collaborative learning environment called SkyApp. To visualise the learning traits of engagement, emotion and motivation, students' inputs and activity data were captured and quantified for analysis. Experiments were first carried…

The Trends in International Mathematics and Science Study (TIMSS) is an international comparative study designed to measure trends in mathematics and science achievement at the fourth and eighth grades, as well as to collect information about educational contexts (such as students' schools, teachers, and homes) that may be related to student…

Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for estimating MIRT models with dichotomous responses have been developed, but constructing an equally efficient and robust algorithm for polytomous models has received limited attention. To address this gap,…

Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…

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…