Background: Students' tendencies to seek feedback are associated with improved learning. Yet, how soon this association becomes robust enough to make predictions about learning is not fully understood. Such knowledge has strong implications for early identification of students at-risk for underachievement via digital learning platforms.…

In this article, we explore the drivers of students' success in a first-year university statistics course. Using a unique sample from Westminster International University in Tashkent, we discover that student engagement with their studies is reflected in their class attendance and in the use of online resources, which continue to play an important…

Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is…

Being able to understand, interpret, and critically evaluate data is necessary for all individuals in our society. Using the PreK-12 Guidelines for Assessment and Instruction in Statistics Education-II (GAISE-II; Bargagliotti et al., 2020) curriculum framework, the current paper outlines five evidence-based recommendations that teachers can use to…

Most current statistics courses include some instruction relevant to causal inference. Whether this instruction is incorporated as material on randomized experiments or as an interpretation of associations measured by correlation or regression coefficients, the way in which this material is presented may have important implications for…

In the complex world of educational policy, causal inferences will be debated. As we review non-experimental designs in educational policy, we focus on how to clarify and focus the terms of debate. We begin by presenting the potential outcomes/counterfactual framework and then describe approximations to the counterfactual generated from the…

This paper illustrates how years 1 and 2 students were guided to engage in data modelling and statistical reasoning through interdisciplinary mathematics and science investigations drawn from an Australian 3-year longitudinal study: "Interdisciplinary Mathematics and Science Learning" (https://imslearning.org/). The project developed…

As COVID-19 continues to impact various business sectors, university administrators have steadily pushed for all academic units to resume on campus operations and activities; conversely, faculty and students have expressed increased interest in continuing online teaching/learning. We aim to mitigate this "tug-of-war" between…

Today's students are surrounded by machine learning (ML)-powered tools. Yet, few understand how they work. While there are numerous opportunities for students to learn about ML in informal settings (e.g., Alvarez et al., 2022; Druga et al., 2022) and online (e.g., code.org, Scratch), there are far fewer opportunities in the United States for…

Data in social and behavioral sciences typically contain measurement errors and also do not have predefined metrics. Structural equation modeling (SEM) is commonly used to analyze such data. This article discuss issues in latent-variable modeling as compared to regression analysis with composite-scores. Via logical reasoning and analytical results…

The present paper investigates one aspect of questionable research practices relating to Arabic L1 learners of foreign languages, namely the use of statistics. The objective of the paper is to argue that reproducible research requires adopting wise practices in linguistics and that the excessive focus on quantification does not seem to serve this…

Despite the growing interest in incorporating response time data into item response models, there has been a lack of research investigating how the effect of speed on the probability of a correct response varies across different groups (e.g., experimental conditions) for various items (i.e., differential response time item analysis). Furthermore,…

Social science datasets usually have missing cases, and missing values. All such missing data has the potential to bias future research findings. However, many research reports ignore the issue of missing data, only consider some aspects of it, or do not report how it is handled. This paper rehearses the damage caused by missing data. The paper…

Examples of the impact of statistical theory on assessment practice are provided from the perspective of a statistician trained in theoretical statistics who began to work on assessments. Goodness of fit of item-response models is examined in terms of restricted likelihood-ratio tests and generalized residuals. Minimum discriminant information…

In the contemporary society a massive amount of data is generated continuously by various means, and they are called Big-Data sets. Big Data has potential and limits which need to be understood by statisticians and statistics consumers, therefore it is a challenge to develop Big-Data Literacy to support the needs of constructive, concerned, and…