Chapter 2 Introduction
Socializing, working, and even teaching and learning are increasingly impacted by data. These sources of data are created by us, for us, and about us. Work with data turns learners from consumers of knowledge to creating knowledge (Hancock, Kaput, & Goldsmith, 1992; Lehrer & Schauble, 2015; Lee & Wilkerson, 2018; Finzer, 2013). Practice with such work empowers learners to ask questions and to answer them with arguments and explanations that draw from data as evidence (McNeill & Krajcik, 2007). This work, then supports learners to create new knowledge in learning environments and classrooms which is an aim of recent reform efforts that cast a vision of learning that emphasizes participation in the practices of STEM disciplines (NGSS Lead States, 2013; National Governors Association Center for Best Practices, Council of Chief State School Officers, 2010).
Work with data includes broad processes of collecting, creating, modeling data, and even asking questions that can be answered with data. This work, then, is more than just crunching numbers. It is also more than interpreting a figure created by someone else. Instead, work with data is about making sense of phenomena in the world–or solving problems in the world. This focus on phenomena is particularly relevant to those designing and enacting learning opportunities focused on work with data (Lee & Wilkerson, 2018; Singer, Hilton, & Schweingruber, 2006; Wild & Pfannkuch, 1999).
Work with data provides a capability that can be used across content areas, particularly in advanced coursework. Aspects of work with data are recognized as core competencies across recent curricular documents for STEM subject area learning. They are found, for example, in the Next Generation Science Standards and the Common Core State Standards. These standards highlight the role of authentic work with data as part of engaging in scientific and engineering and mathematical practices, respectively. These capabilities may be particularly useful in STEM domains because advanced coursework in these domains often involves demanding and abstract work with data, work that may be more accessible to more learners when they encounter it earlier in their education.
Past research on work with data has mostly been set in mathematics contexts and has focused on mathematical practices, like generating measures of phenomena and creating data models (English, 2012; Lehrer & Romberg, 1996; Lesh, Middleton, Caylor, & Gupta, 2008). It has often focused on specific cognitive outcomes (e.g., Gelman & Markman, 1987), strategies to support work with data (Petrosino, Lehrer, & Schauble, 2003), and some opportunities and challenges facing both teachers and learners when working with data (e.g., Konold & Pollatsek, 2002; Finzer, 2013). There has been some research about work with data in science settings, too. However, scholarship has pointed out that what it means to work with data can vary greatly in actual classrooms and other learning environments (McNeill & Berland, 2017). Even so, this past research broadly suggests that engaging in work with data is powerful concerning learning both about and how to do mathematics and science (Lee & Wilkerson, 2018; Lehrer & Schauble, 2015). Lehrer and Schauble (2015), summarizing past research on the use of mathematical practices in science contexts, note that work with data “has an exceptionally high payoff in terms of students’ scientific reasoning” (p. 696).
To date, past research shows that using a framework from contemporary engagement theory to characterize students’ experiences has been informative both in research and to practicing educators. Work with data is similar to hands-on, laboratory work which research has shown to be engaging to students (Schmidt, Rosenberg, & Beymer, 2018). In addition, work with data is demanding and requires sustained effort and focus (Lehrer & Schauble, 2015; National Research Council, 2015), and past work has shown that when learners are more challenged (and competent), they are more likely to be engaged (Schneider et al., 2016; Shernoff et al., 2016). Knowing more about how youth engage in work with data is valuable as engagement is a meaningful outcome for STEM learners in its own right (Sinatra, Heddy, & Lombardi, 2015). It may also be an antecedent of changes in other outcomes, such as their well-being, achievement, and the pursuit of an area of study or career (Wang, Chow, Hofkens, & Salmela-Aro, 2015; Wang & Eccles, 2012). However, research has not examined engagement in work with data. Because engaging in work with data seems to be so potentially beneficial to learners, better understanding the nature of work with data and learners’ engagement in such practices is needed.
The purpose of this study, then, is to examine youth engagement in a variety of learning activities that involve work with data. I explore youths’ engagement in the context of outside-of-school STEM enrichment programs carried out during the summer, and I consider work with data through the lens of specific aspects identified from past research, such as asking questions and generating and modeling data. Such settings (in outside-of-school programs) are an especially useful context for exploring work with data because they can be designed around youths’ interests (Lauer, Akiba, Wilkerson, Apthorp, Snow, & Martin-Glenn, 2006). One promise of work with data in outside-of-school settings is that relevant sources of data can be inherently interesting to learners. Such sources of data can be used as a context for learning about the world, allowing youth to ask and answer personally and socially meaningful questions, whereas many outside-of-school programs are focused around commercial aims, such as developing mobile device applications. Knowing more about how youth engage can also provide a foundation for subsequent work to explore how particular curricula and engaging experiences for youth spark their interest in work with data, including hobbies and occupations related to data science, but also in STEM domains in general.