This toolkit is designed to guide educators in developing and improving practical measurement instruments for use in networked improvement communities (NICs) and other education contexts in which principles of continuous improvement are applied. Continuous improvement includes distinct repeating processes: understanding the problem, identifying…

Data-informed decision-making in teachers' practice, now recommended by different teacher inquiry models and policy documents, implies deep practice change for many teachers. However, not much is known about how teachers perceive the different steps that analytics-informed teacher inquiry entails. This paper presents the results of a study into…

Protection of student privacy is critical for scaling up the use of learning analytics (LA) in education. Poorly implemented frameworks for privacy protection may negatively impact LA outcomes and undermine trust in the discipline. To design and implement models and tools for privacy protection, we need to understand privacy itself. To develop…

Students reasoning with data in an authentic science environment had the opportunity to learn about the process of science and the world around them while developing skills to analyze and interpret self-collected and secondhand data. Our results show that nearly 50% of the treatment group responses were accurate when describing the reason for…

Purpose: The critical incident technique (CIT) is widely used in many disciplines; however, scholars have acknowledged challenges associated with analyzing qualitative data when using this technique. Therefore, the purpose of this article is to address the data analysis issues that have been raised by introducing some different contemporary ways…

The last decades have seen an unprecedented growth in the availability and accessibility of data, highly influenced by the ubiquity of digital media and the internet. As society contends with data's increasing impact on the nature of knowledge, communication, and privacy, it faces a pressing need for citizens who are intelligent producers and…

Estimating prescription medicine use is challenging due to recall bias associated with surveys and coverage bias in administrative data. This study assesses how making operational improvements and combining both survey and administrative data sources can increase data quality on filled prescriptions. We use data from the Medicare Current…

Multivariate Functional Principal Component Analysis (MFPCA) is a valuable tool for exploring relationships and identifying shared patterns of variation in multivariate functional data. However, interpreting these functional principal components (PCs) can sometimes be challenging due to issues such as roughness and sparsity. In this dissertation,…

Systematic reviews are a time-consuming yet effective approach to understanding research trends. While researchers have investigated how to speed up the process of screening studies for potential inclusion, few have focused on to what extent we can use algorithms to extract data instead of human coders. In this study, we explore to what extent…

The five SEERNet digital learning platforms (DLPs) present unique opportunities for researchers by offering tools, processes, and infrastructure to make research more efficient, scalable, and relevant. However, conducting research within a DLP may require a shift in a researcher's orientation or mindset in how they think about potential research…

This study is a meta-analysis of overall recruitment rate (ORR) in probability-based online panel research. In this study, we included 23 general population probability-based online panels (out of 28 identified and described) covering 15 countries and provided a comprehensive overview of their methodological approaches to recruitment. We…

This paper presents a unique advanced statistical approach based on Artificial Intelligence (AI) to examine factors affective on phonological awareness and print awareness of preschool children. Artificial Neural Network (ANN) models were created and correlations between the independent and dependent (outcome) variables were analyzed. The ANN…

In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem…

Balancing speed and quality during crises pose challenges for ensuring the value and utility of data in social science research. The COVID-19 pandemic in particular underscores the need for high-quality data and rapid dissemination. Given the importance of behavioural measures and compliance with measures to contain the pandemic, social science…

Higher education is awash with data that, when refined, facilitates data-informed decisions. Such decision-making is much more prevalent in support of undergraduate education given the much larger number of undergraduates pursuing higher education in contrast to the much smaller proportion of graduate students. A simple extension of current…