## Practice 4: Analyzing and Interpreting Data

When analyzing and interpreting data in the classroom, students may work in teams, receive feedback from others, and collectively communicate results. Instructional strategies that support students’ feelings of belonging cultivate a safe space for students to hone skills for analyzing and interpreting data in these regards. Strategies that support belonging also encourage students to develop a sense of being part of a community of scientists and engineers, which is especially important for students who may not have a well-developed science identity or who may feel alienated from science [see Motivation as a Tool for Equity]. As students begin to feel a greater sense of belonging within their science classroom community and within science and engineering communities, they may feel more inclined to engage in the practices of analyzing and interpreting data.

## Strategies

- Set up norms for these conversations to establish a sense of belonging/comfort around how to analyze, interpret, and communicate results as evidence.

There is likely a wide variety of math ability levels in a single science class. Differences in skill may require different levels of scaffolding in order to develop confidence for all students. Some students may have little experience with data or may have limited confidence in successfully being able to tabulate, graph, or perform statistical analysis on data. Students may also be uncomfortable presenting the results of the analysis and interpretation to their peers. Supporting students’ confidence as they engage in these activities will be crucial for them to feel comfortable working with data.

## Strategies

- Guidelines for graphing: a checklist for the parts of a graph, thinking guides for students to determine a good scale for the data they are graphing or the type of graph that will be most useful for their purpose
- Guidelines for data tabulation: checklists for how to set up a frequency table, how to set up a table for the different variables in an experiment, etc.
- Guidelines for summarizing data: different summary statistics (e.g., mean, median, mode) and what information they provide scientists and engineers

Data collection, especially by young scientists and engineers with limited experience, can contain a large amount of error. During analysis, having a learning orientation will help frame that error as a critical part of the learning experience (both learning techniques for how to reduce the error in future data collection and learning how to account for error in data interpretation) rather than a failure. Supporting a learning orientation will also help with encouraging students to engage deeply with their data to make sense of a phenomenon or design solutions to design problems, rather than merely performing calculations or getting the “right” answer.

## Strategies

Cognitive autonomy is especially important to encourage students to make their own decisions about how to analyze or make sense of the data, as well as to generate alternative interpretations and explanations. Working with data might make teachers prone to undermine student autonomy if they suggest that there is a clear “right” answer, such as a predetermined set of similarities and differences between two data tables that the teacher is leading students to identify. There might also tend to be an overemphasis on smaller autonomy allowances (e.g., letting students choose the colors for a graph) without accompanying demands on students’ cognitive autonomy in making sense of phenomena or problem solving. It is important to provide sufficient time and scaffolding for students to engage in rigorous, autonomous sense-making through the analysis and interpretation of data.

## Strategies

Some students may have lower confidence in their ability to tabulate, graph, or perform statistical analyses on data or may even think they are not a “math person.” Framing data analysis within a phenomenon or design problem that is of interest to students may help motivate them to work hard on the mathematics needed for data analysis. Connecting the practice of analyzing and interpreting data to the work of scientists and engineers may help encourage students to see the value of math as a tool to make sense of the world. Encouraging students to connect data analysis and interpretation to a broad range of situations that relate to their lives and home communities can make them more invested in the practice as something that can be leveraged to figure out phenomena or solve problems that feel relevant and important to them [see Motivation as a Tool for Equity].