This work reports on the implementation of a self-contained data-literacy exercise designed for use in undergraduate classes to help students practice data literacy skills such as interpreting and evaluating evidence and assessing arguments based on data. The exercises use already developed data-visualizations to test and develop students' ability…

Graphical representations appear everywhere in modern society. Their ubiquity has, in part, justified graph literacy as a core competency in education. Graph literacy - the capacity to read and interpret graphs - consists of understanding features, identifying variables, interpreting trends, and forming real-world connections. Experts and novices…

This article describes an activity and resource from The New York Times that can be used to help learners cultivate critical statistical literacy. Critical statistical literacy involves understanding, interpreting, and questioning statistical information to make informed decisions (Casey et al., 2023; Franklin et al., 2015; Weiland, 2017). It is a…

This study focuses on the problem that the process of building learning habits has not been clearly described. Therefore, we aim to extract the stages of learning habits from log data. We propose a data model to extract stages of learning habits based on the transtheoretical model and apply the model to the learning logs of self-directed extensive…

Data analysis and interpretation has always played a fundamental role in the scientific curricula of high school students. The spread of digitalization has further increased the number of learning environments whereby this topic can be effectively taught: as a matter of fact, the ever-growing diffusion of data science across diverse sectors of…

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…

School science is often very different from "real world" science. One important difference, and possibly the main one, is that in school science the relationships between variables have often been sanitized -- essentially "cleaned up" -- so that there is very little (and often no) variation in the data from the relationship…

We present a unique and innovative course, Biostatistical Literacy, developed at the University of Minnesota. The course is aimed at public health graduate students and health sciences professionals. Its goal is to develop students' ability to read and interpret statistical results in the medical and public health literature. The content spans the…

Driven by the increased availability of Learning Management System data, this study explored its value and sought understanding of student behaviour through the information contained in activity level log data. Specifically, this study examined analytics data to understand students' engagement with online videos. Learning analytics data from the…

Students often struggle with the relationship between mathematical graphs and the data they represent. To truly understand types of evolutionary selection, students need to be proficient with several different skills in math, science, and literacy contexts. With math, students must be able to identify variables, design appropriate graphs based on…

Authentic, "messy data" contain variability that comes from many sources, such as natural variation in nature, chance occurrences during research, and human error. It is this messiness that both deters potential users of authentic data and gives data the power to create unique learning opportunities that reveal the nature of science…

Drawing valid inferences from modern measurement models is contingent upon a good fit of the data to the model. Violations of model-data fit have numerous consequences, limiting the usefulness and applicability of the model. As Bayesian estimation is becoming more common, understanding the Bayesian approaches for evaluating model-data fit models…

Given growing interest in K-12 data and data science education, new approaches are needed to help students develop robust understandings of and familiarity with data. The model of the "quantified self"--in which data about one's own activities are collected and made into objects of study--provides inspiration for one such approach. By…

Could the research you read be fundamentally flawed? Could critical defects in methodology slip by you undetected? To become informed consumers of research, students need to thoughtfully evaluate the research they read rather than accept it without question. This second edition of a classic text gives students the tools they need to apply critical…

Learning about people from long ago and far away poses a challenge for students because such people seem so distant and different. The lack of easily comprehensible text-based primary sources compounds this problem. Using a built environment as a primary source makes people from the distant past more accessible, concrete and exciting. Broadly…