Data Explorations in Ecology

The Data Explorations in Ecology Project supports students and teachers in gaining proficiency with data exploration practices.

The project included curriculum development and a professional development (PD) effort designed to address students' data literacy issues through the use of place based, evidence driven, inquiry-based activities that involve the use of both first and second hand ecological data. 

The Data Explorations in Ecology Project (DEEP), was a professional development (PD) effort and curriculum development project designed to address data literacy issues through the development and implementation of a series of curricular modules designed to support teachers as they engage students in place based, evidence driven, inquiry-based activities that involve the use of both first and second hand data to explore locally relevant ecological issues. The research findings highlight students’ knowledge, skills and attitudes toward a variety of data exploration activities, including interpreting data representations, understanding variability and evaluating claims based on evidence.

National and international scientific and education groups recognize the need for a scientifically literate citizenry that can apply science to important environmental issues in order to make sound decisions and create policies that protect ecosystems (NRC 2012; AAAS 1993; ACERE 2009). Because of this, citizens are being asked to become critical consumers of information so that they can make pivotal decisions about the environment based on a plethora of arguments in their schoolwork, in the media, and as they vote and make everyday choices. However, the arguments they encounter often have weak scientific reasoning, limited supporting evidence and many types of bias. In light of this challenge, developing a scientifically literate citizenry requires training our students to think critically and develop an understanding of how evidence is used to construct, support, and evaluate claims (Luykx & Lee 2007; Sampson & Clark, 2008).

Both the Next Generation Science Standards (NGSS Lead States, 2013) and the Common Core Standards (Common Core State Standards Initiative, 2010) address this need through an emphasis on inquiry-based practices such as constructing explanations, engaging in argument from evidence, and evaluating information. However, studies in both science and mathematics education have shown that students often struggle with data exploration skills related to these practices, including understanding variability in data (Ben-Zvi, 2004; Reading, 2004; delMas & Liu, 2005), interpreting and using statistical information (Garfield & Ben-Zvi, 2004), reasoning based on evidence (Driver et al., 2000), and extracting pertinent information from graphical representations (Bieda & Nathan, 2006; Friel et al., 2001). People’s understandings of variability and its implications are of particular concern to educators because these skills are a foundational aspect of statistical reasoning and are essential for understanding the degrees of certainty with which claims can be made (Zawojewski & Shaughnessy, 2000). In a very direct way, the difficulties students experience with these skills may hinder their progress toward becoming the environmentally literate citizens and critical consumers of information that today’s environmental problems require.

In order to address data literacy issues in secondary science classrooms, we developed and implemented a series of curricular modules designed to support students in gaining proficiency with data exploration practices. These modules engaged students in place based, evidence driven, inquiry-based activities that involved the use of both first and second hand data to explore locally relevant ecological issues. The data exploration practices focused on included a variety of skills essential to the practices of constructing, defending, and evaluating evidence-based claims. These skills include, but are not limited to identifying data representations, choosing appropriate data representations, interpreting data representations, identifying potential sources of variability, understanding the implications of variability, understanding how variability can influence claims, and understanding how to evaluate claims based on the available evidence. The focus of this study is on understanding students’ proficiency with these practices across grade levels. We were also interested in gaining insight into students’ interest in, and attitudes toward, data exploration practices. Our hypothesis was that explicit instruction and practice with data exploration practices would not only increase students’ interest and proficiency with these skills, but also result in students’ increased ability to construct, defend, and evaluate claims based on data. The specific questions addressed in this study include:

1. What do secondary students know, and what are they able to do, in terms of data exploration practices in science contexts?
2. What are secondary students’ interests and attitudes toward data exploration practices?
3. How do students’ knowledge, skills and attitudes towards data exploration practices compare across the upper and lower grade levels in secondary schools