The literature on educational choices and participation has tended to focus on youth and to some extent on older adults, with little attention to the group in between. This paper therefore analyses participation in various types of learning by people in their 30s and 40s. This group are often juggling career and family life commitments meaning…

Textbooks have a long history of explicitly and implicitly supporting heteronormative ideologies and effectively erasing marginalized populations, such as people who identify as queer, by exclusion. Students view textbooks as an authority over content and spend large portions of in and out of class time interacting with their textbooks. Because…

There is increased use of Bayesian networks (BN) in educational assessment. In psychometrics, BN serves as a measurement model with high flexibility, suitable to model educational assessment data with a complex structure. BN is a novel psychometric approach and not all aspects of its application are well-known. The article aims to provide the…

Academic libraries, together with their colleges and universities, are increasingly identifying first-generation college students as an underserved population that is likely to experience barriers to library access and usage. Less is known, however, about the information literacy skills of first-generation students, particularly in comparison with…

Collaborative work is inherent to being a statistician or data scientist, yet opportunities for training and exposure to real-world scenarios are often only a small part of a student's academic program. Resources to facilitate effective and meaningful instruction in communication and collaboration are limited, particularly when compared to the…

This paper investigates two specific probabilistic biases which middle graders usually exhibit when reasoning about probability and randomness on assessment items. We discuss how students' reasoning about key probability concepts undergirds statistics literacy related to randomness, independence, and the likelihood of future events based on past…

In a world where everything involves data, an application of it became essential to the decision-making process. The Case Method approach is necessary for Data Science education to expose students to real scenarios that challenge them to develop the appropriate skills to deal with practical problems by providing solutions for different activities.…

Lord's paradox arises from the conflicting inferences obtained from two alternative approaches that are typically used in evaluating the treatment effect using a pre-post test design. The two main approaches for analyzing such data are: (1) to regress the change from pretest to posttest on the treatment indicator (change score approach; CS); (2)…

Bayesian techniques have become increasingly present in the social sciences, fueled by advances in computer speed and the development of user-friendly software. In this paper, we forward the use of Bayesian Asymmetric Regression (BAR) to monitor intervention responsiveness when using Curriculum-Based Measurement (CBM) to assess oral reading…

Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA)…

The theoretical reason for the presence of differential item functioning (DIF) is that data are multidimensional and two groups of examinees differ in their underlying ability distribution for the secondary dimension(s). Therefore, the purpose of this study was to determine how much the secondary ability distributions must differ before DIF is…

We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the correctness of students' responses to questions using historical clickstream data. This model combines the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender Systems. By leveraging clickstream…

Many education researchers have advocated grounding abstract mathematical and scientific concepts in students' lived experiences, environmental interactions, and perceptions. This dissertation explores the causal effects of various grounding strategies in instructional settings, specifically on the topic of statistical sampling. The first chapter…

In a fully online college introductory statistics class, we incorporated a flexible synchronous course component to provide instructor immediacy and support students who are lacking sufficient prior knowledge when a new topic is introduced. We describe a learning cycle with weekly team quizzes inspired by the readiness assurance process of…

Mathematics education research has been slow to adopt inclusive practices concerning Queer students. In this study, two doctoral dissertations highlight responsible research approaches to advance inclusivity. Ambiguous use of the terms "sex" and "gender" and using only binary statistical methods can lead to erroneous…