Responsible citizenship and sound decision-making in today's information age necessitate an appreciation of the role of uncertainty in the process of generating data-based scientific knowledge. The latter calls for coordinating between different types of uncertainties, related to three types of relevant reasoning: statistical, scientific, and…

Many generalization studies in education are typically based on a sample of 30-70 schools while the inference population is at least twenty times larger. This small sample to population size ratio limits the precision of design-based estimators of the population average treatment effect. Prior work has shown the potential of small area estimation…

Interrupted time series (ITS) are often meta-analysed to inform public health and policy decisions but examination of the statistical methods for ITS analysis and meta-analysis in this context is limited. We simulated meta-analyses of ITS studies with continuous outcome data, analysed the studies using segmented linear regression with two…

A workshop in environmental sustainability was organized to develop the skills and competencies in mitigating climate change, adapting climate risk management, managing waste, adapting sustainable human settlements and using sustainable transport, and developing skills of reducing CO2 emissions, and conserving energy and ecosystem, & educating…

This research presents the results of the implementation of a model-eliciting activity called Brickyards, designed to promote the learning of the binomial distribution. The theoretical framework used was the Models and Modeling Perspective, and the participants were undergraduate students enrolled in a probability and statistics course of the…

Various sources suggest preparing teachers of early- or primary-age students to teach probability and statistics involves various challenges. Some of the approaches researchers take for resolving these challenges include developing preservice teacher content knowledge, pedagogical knowledge, as well as providing other opportunities to learn.…

Math anxiety has become an alarming social justice concern, as it results in negative academic consequences, contributes to disinterest and lack of persistence in STEM programs for underrepresented students, and limits their opportunities in STEM careers. According to research, this fear of math occurs long before students begin working on math…

In education, machine learning (ML), especially deep learning (DL) in recent years, has been extensively used to improve both teaching and learning. Despite the rapid advancement of ML and its application in education, a few challenges remain to be addressed. In this thesis, in particular, we focus on two such challenges: (i) data scarcity and…

We investigated how the transition to remote instruction during the COVID-19 pandemic affected students' engagement, self-appraisals, and learning in advanced placement (AP) Statistics courses. Participants included 681 (M[subscript age]=16.7 years, SD[subscript age]=0.90; %female=55.4) students enrolled in the course during 2017-2018 (N=266),…

Social scientists seeking to inform policy or public action must carefully consider how to identify effects and express inferences because actions based on invalid inferences will not yield the intended results. Recognizing the complexities and uncertainties of social science, we seek to inform inevitable debates about causal inferences by…

As psychology grows in popularity, most students select professions related to therapy, which constantly deem statistics courses as irrelevant and accentuate negative attitudes. This study explores perspectives of knowledge, also known as epistemological frames, that students enrolled in a psychology-based course in statistics generate and the…

This paper presents a tool for creating student models in logistic regression. Creating student models has typically been done by expert selection of the appropriate terms, beginning with models as simple as IRT or AFM but more recently with highly complex models like BestLR. While alternative methods exist to select the appropriate predictors for…

This paper presents a tool for creating student models in logistic regression. Creating student models has typically been done by expert selection of the appropriate terms, beginning with models as simple as IRT or AFM but more recently with highly complex models like BestLR. While alternative methods exist to select the appropriate predictors for…

Mixed Membership Models (MMMs) are a popular family of latent structure models for complex multivariate data. Instead of forcing each subject to belong to a single cluster, MMMs incorporate a vector of subject-specific weights characterizing partial membership across clusters. With this flexibility come challenges in uniquely identifying,…

This short article continues the exploration of the Common Online Data Analysis Platform (CODAP) and statistics begun in the previous article "Statistical Investigations and CODAP, Part 1: EDA." In Part 2, the author discusses the teaching of statistical inference focusing on activities for the senior secondary years. In particular, the…