Data preparation (also called "wrangling" or "cleaning") -- the evaluation and manipulation of data prior to formal analysis -- is often dismissed as a precursor to meaningful engagement with a dataset. Here, we re-envision data preparation in light of calls to prepare students for a data-rich world. Traditionally, curricular…

Despite the rising popularity of big data, there is speculation that evaluators have been slow adopters of these new statistical approaches. Several possible reasons have been offered for why this is the case: ethical concerns, institutional capacity, and evaluator capacity and values. In this method note, we address one of these barriers and aim…

In our data-driven society, it is essential for students to become statistically literate. A core domain within Statistical Literacy is Statistical Inference, the ability to draw inferences from sample data. Acquiring and applying inferences is difficult for students and, therefore, usually not included in the pre-10th-grade curriculum. However,…

Purpose: Higher education institutions increasingly emphasize data analytics education, yet curricula based solely on competency-based frameworks may overlook industry's process-driven approach. This study examines the process deficit in data analytics education and its impact on workforce readiness. It explores strategies to better align…

This Forum Guide, developed by the National Forum on Education Statistics, provides best practices for collecting, managing, and using educator workforce data. It offers education agencies practical guidance on building high-quality data systems that inform teacher recruitment, retention, and professional development efforts.

Use and analysis of, and reflection on, qualitative and quantitative data helps illuminate the journey of professional learning for educators and students. This article describes methods to provide reflective space for administrative teams to listen to stakeholders' stories and consider additional support they can put in place to help their staff…

Evaluation data helps inform decision-makers about the time, human capital, and funding required for professional learning to be effective. Evaluation data also guides program improvement and sets leaders' expectations for ongoing monitoring and accountability. The complexities of the educational systems in which professional learning happens mean…

This study explores the potential to enhance the performance of convolutional neural networks (CNNs) for automated scoring of kinematic graph answers through data augmentation using Deep Convolutional Generative Adversarial Networks (DCGANs). By developing and fine-tuning a DCGAN model to generate high-quality graph images, we explored its…

In educational systems, predictive models face significant challenges during initial deployment and when new students begin to use them or when new exercises are added to the system due to a lack of data for making initial inferences, often called the cold start problem. This paper tests logitdec and logitdecevol, "evolutionary" features…

In an era increasingly defined by the proliferation of big data and its transformative impact on decision-making across disciplines, the ability to interpret and engage with complex datasets has become an essential skill for future generations. This literature review reconceptualises data visualisation through the lens of school education and…

In today's online data-driven world, people constantly shed data and deposit digital footprints. When individuals access health services, governments and health providers collect and store large volumes of health information about people that can later be retrieved, linked and analysed for research purposes. This can lead to new discoveries in…

This dissertation address two significant challenges in the causal inference workflow for Big Observational Data. The first is designing Big Observational Data with high-dimensional and heterogeneous covariates. The second is performing uncertainty quantification for estimates of causal estimands that are obtained from the application of black box…

Government agencies collect vast amounts of administrative data in their day-to-day activities, primarily for program operations. But the information is less often used as a research tool or fully harnessed for its evidence-building potential. This brief is the fourth in a series of publications from MDRC about the Temporary Assistance for Needy…

The prediction of student performance and the analysis of students' learning behaviour play an important role in enhancing online courses. By analysing a massive amount of clickstream data that captures student behaviour, educators can gain valuable insights into the factors that influence students' academic outcomes and identify areas of…

Conducting high-quality overviews of reviews (OoR) is time-consuming. Because the quality of systematic reviews (SRs) varies, it is necessary to critically appraise SRs when conducting an OoR. A well-established appraisal tool is A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2, which takes about 15-32 min per application. To save time,…