Academic journal article The Science Teacher

Thinking with Data: Students Use Authentic Data to Form Evidence-Based Conclusions and Think like a Scientist

Academic journal article The Science Teacher

Thinking with Data: Students Use Authentic Data to Form Evidence-Based Conclusions and Think like a Scientist

Article excerpt

For students to be successful in STEM, they need statistical literacy, the ability to interpret, evaluate, and communicate statistical information (Gal 2002). The science and engineering practices dimension of the Next Generation Science Standards (NGSS) highlights these skills, emphasizing the importance of students' ability to analyze data and form evidence-based conclusions in response to complex questions. Recent research suggests that most high school students can create a graph and calculate a mean but cannot describe what data represent, reason about data, or use data to generate evidence-based conclusions (Bakker and Gravemeijer 2004; Garfield and Ben-Zvi 2007; Sampson, Enderle, and Grooms 2013). This research recommends shifting instructional focus toward using more intuitive notions to make sense of data, detect patterns, and use data to confirm or refute scientific hypotheses before moving to more precise statistical definitions and calculations.

Correspondingly, the Science, Biostatistics, and Cancer Education (SBCE) Distributions Module was developed by a team including education researchers (the authors of this article), a statistician, software engineers, and high school teacher leaders to provide students with the tools to informally reason about distributions of science-related data to form evidence-based arguments while building a bridge to formal statistical instruction.

The SBCE Distributions Module

The module first introduces students to data, discussing what data are and why data are needed to make accurate conclusions. Then it introduces distributions of data displayed in stacked dotplots and the statistical terminology used to describe distributions. Finally, the module teaches basic resampling procedures, a more transparent approach for assessing statistical differences between two distributions of data (Cobb 2007).

The SBCE module consists of three teacher presentations, two student activities, and two case studies and takes approximately six to eight classes to complete. The module (see overview, Figure 1, p. 60) provides opportunities for students to apply the NGSS science and engineering practices with emphasis on Analyzing and Interpreting Data and Engaging in Argument From Evidence.

Throughout the module, students use SeeIt, an online tool that enables them to display, analyze, and compare distributions in dotplots and make statistical inferences using resampling techniques. SeeIt is preloaded with all data necessary for the case studies, and users can also upload their own data. SeeIt, as well as other needed materials, including teacher presentations, activities, and case studies, are freely available online (see "On the web"). Materials include teacher presentations, complete with slides, embedded videos, corresponding speaker notes, and an assessment tool, as well as a student page (see "On the web") where students can access all materials and data needed for the module.

High school biology, mathematics, and statistics teachers tested the module and provided feedback. This article highlights one presentation, student activity, and case study from the module.

Sample presentation: Thinking with data

The presentation begins with the teacher asking, "What are data?" and "How are data useful?" and then proposing the discussion question: "Are teenagers more healthy than they were 20 years ago?" The provided speaker notes offer teacher prompts to stimulate discussions, asking how students would define "healthy," what they would measure to assess health, and what they would expect these data to look like if teenagers were in fact healthier today. The purpose is to stimulate class discussion about what data are needed and how they can be used to answer this type of question.

Students are then shown two short videos about why data are necessary for drawing conclusions and how, without careful examination of data, people often jump to the wrong conclusions. …

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