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Over the last decade, reform in science education has placed an emphasis on the science practices as a way to engage students in the process of science and improve scientific literacy. A critical component of developing scientific literacy is learning to apply quantitative reasoning to authentic scientific phenomena and problems. Students need practice moving fluidly (or fluently) between math and science to develop a habit of mind that encourages the application of quantitative reasoning to real-world scenarios. Here we present a student-facing model that challenges students to think across these two fields. The model brings together math and science with a goal to increase scientific literacy by engaging students in quantitative reasoning within the context of scientific questions and phenomena. In the classroom, the model serves to help students visualize the logical and necessary moves they make as they use quantitative reasoning to connect science practices with mathematical thinking. 

Despite broad consensus that highlighting counter-stereotypical scientist role models in educational materials promotes equity and success, the specific elements that make these materials effective remain untested. Are pictures of counter-stereotypical scientists enough to communicate to students that scientists come from a variety of backgrounds, or is additional information required? To parse the effects of including visual depictions and humanizing information about scientists featured in biology course materials, we distributed three randomized versions of assignments over several academic terms across 36 undergraduate institutions (n> 3700 students). We found that including humanizing information about scientists was key to increasing student engagement with the biology course materials. The positive effect of humanizing information was especially important for students who related to the scientists. Structural equation modelling revealed the extent to which students related to scientists mediated the positive effect of humanizing descriptions on student engagement. Furthermore, our results were strongest among students who shared one or more excluded identity(s) with the featured scientists. Our findings underscore the importance of providing students with examples of humanized and relatable scientists in classrooms, rather than simply adding a photo to increase representation. 

Featuring a diversity of scientists within curriculum provides opportunities for students to relate to them. We manipulated the amount and type of information students received about scientists. We found including personal, humanizing information increased the extent to which students related to them, with implications for curriculum development. 

In this essay, we review how counter-stereotypical scientists have been featured in life science courses and discuss the benefits and costs of developing and interacting with these materials from the perspectives of three groups: students, instructors, and the featured scientists. 

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 itself. While the value of bringing contemporary research and messy data into the classroom is recognized, implementation can seem overwhelming. We discuss the importance of frequent interactions with messy data throughout K–16 science education as a mechanism for students to engage in the practices of science, such as visualizing, analyzing, and interpreting data. Next, we describe strategies to help facilitate the use of messy data in the classroom while building complexity over time. Finally, we outline one potential sequence of activities, with specific examples, to highlight how various activity types can be used to scaffold students' interactions with messy data. 

Data are becoming increasingly important in science and society, and thus data literacy is a vital asset to students as they prepare for careers in and outside science, technology, engineering, and mathematics and go on to lead productive lives. In this paper, we discuss why the strongest learning experiences surrounding data literacy may arise when students are given opportunities to work with authentic data from scientific research. First, we explore the overlap between the fields of quantitative reasoning, data science, and data literacy, specifically focusing on how data literacy results from practicing quantitative reasoning and data science in the context of authentic data. Next, we identify and describe features that influence the complexity of authentic data sets (selection, curation, scope, size, and messiness) and implications for data-literacy instruction. Finally, we discuss areas for future research with the aim of identifying the impact that authentic data may have on student learning. These include defining desired learning outcomes surrounding data use in the classroom and identification of teaching best practices when using data in the classroom to develop students’ data-literacy abilities.