Analyzing and interpreting data

Strategies to promote Belonging with Data:

Building a sense of community is the key to promoting belonging when analyzing and interpreting data. Generally the way to do that is to set up plenty of opportunities for students to share out and receive feedback. When students have collected data they can share that out with their peers on the board, digitally, in a jigsaw, or in a ‘lab meeting’. In addition, student groups can be assigned trials or different testable variables so that when they share out they can create meaning as a group. Students can share out and receive feedback on sources of error, patterns they identified, possible outliers, etc. Promoting belonging in these ways doesn’t mean dispersing responsibilities for data collection but instead makes students feel as though their contributions add to a larger understanding of phenomena. To learn more visit here.

Strategies to promote Confidence with Data:

Building confidence is all the more important for this science and engineering practice because analyzing data requires different levels of math and students will be coming to your classroom with different understandings of using math for analyzing data. In this situation, teachers should work to create as many entry points as possible with questions such as “what do you notice about the data?”, “What wonderings do you have?”, “What might be missing from the data?”, or “What patterns do you see?”. Questions like these ask students to engage with the data regardless of their math skills. In addition, students can benefit from sentence stems as they become more comfortable talking about data. Sentence stems could take the form of “I notice that…” or “This outlier may…” or “The data seems to show…”. Lastly, when showing data visually like graphs or tables, it can be helpful to grow students' confidence to practice with simpler data sets and to provide checklists for completion. For example, when students create a graph, a checklist can help them make sure they selected the right type of graph, it's properly labeled, and shows any stats that would be helpful (Mean, median, deviation, etc.). To learn more visit here.

Promoting data Learning Orientation:

For students to develop a learning attitude for analyzing and interpreting data they need opportunities to examine ‘failure’ and error as much as possible. Students should have plenty of practice in examining data, asking questions about it, identifying sources of error, and interpreting meaning. Teachers should model how to examine data and how to best think through sources of error without relying heavily on ‘human error’. Helping students recognize that error is a normal part of data and science in general will help students build autonomy and confidence as well! To learn more visit here.

Strategies to promote Autonomy with Data:

In analyzing and interpreting data, students need plenty of processing and ‘fiddling with the numbers’ time. Developing independence requires for students to keep the big picture in mind while looking at and manipulating how they view the data. To set students up for success, they should practice different types of data visualizations, practice the math/stats required and even journal individually about what they see. Students should practice interpreting data on their own as much as possible before sharing out with a larger group. This helps them grapple with data and also means no students should feel as though they are being “cold called” in a larger group discussion. Independent practice can be supported with sentence stems, examples, and checklists. Once they have practiced enough, students should be given ample opportunities to make decisions about how to present and interpret data. To learn more visit here.

Strategies to emphasize Relevance in Data:

Making analyzing and interpreting data relevant to students is becoming increasingly important with AI and other technology. Students are continually being faced with data around them and its their skills they learn in school that can help them make sense of it all. Students should be confronted with data all the time in their science class even if it's a small set (nutritional labels) as a warm up, a large set such as from NOAA or NASA, or even their own classroom data. You can make data relevant by showing examples of times data has led to a huge scientific discovery (such as Katherine Johnson’s NASA calculations). You can also make the data feel relevant by connecting to the local community (for example, population density and pathogen transfer). It can even be useful for students to bring random data they come across on social media (elections, crime rates, mental health, their own algorithm, etc!) and for the class to dissect it to make sense of the conclusion. To learn more visit here.

Standard Name: HS-ESS3-5 Earth and Human Activity

Standard: Analyze geoscience data and the results from global climate models to make an evidence-based forecast of the current rate of global or regional climate change and associated future impacts to Earth systems.

Observable Features of Student Performance by the end of the Course: 

1. Organizing data

• Students organize data (e.g., with graphs) from global climate models (e.g., computational simulations) and climate observations over time that relate to the effect of climate change on the physical parameters or chemical composition of the atmosphere, geosphere, hydrosphere, or cryosphere.

• Students describe* what each data set represents.

2. Identifying relationships

• Students analyze the data and identify and describe* relationships within the datasets, including: 

  • • Changes over time on multiple scales; and 

    • Relationships between quantities in the given data.

3. Interpreting data

• Students use their analysis of the data to describe* a selected aspect of present or past climate and the associated physical parameters (e.g., temperature, precipitation, sea level) or chemical composition (e.g., ocean pH) of the atmosphere, geosphere, hydrosphere or cryosphere.

• Students use their analysis of the data to predict the future effect of a selected aspect of climate change on the physical parameters (e.g., temperature, precipitation, sea level) or chemical composition (e.g., ocean pH) of the atmosphere, geosphere, hydrosphere or cryosphere.

• Students describe* whether the predicted effect on the system is reversible or irreversible.

• Students identify one source of uncertainty in the prediction of the effect in the future of a selected aspect of climate change.

• In their interpretation of the data, students: 

  • • Make a statement regarding how variation or uncertainty in the data (e.g., limitations, accuracy, any bias in the data resulting from choice of sample, scale, instrumentation, etc.) may affect the interpretation of the data; and 

    • Identify the limitations of the models that provided the simulation data and ranges for their predictions.

Standard Name: HS-ESS2-2 Earth's Systems

Standard: Analyze geoscience data to make the claim that one change to Earth's surface can create feedbacks that cause changes to other Earth systems.

Observable Features of Student Performance by the end of the Course: 

1. Organizing data

• Students organize data that represent measurements of changes in hydrosphere, cryosphere, atmosphere, biosphere, or geosphere in response to a change in Earth’s surface.

• Students describe* what each data set represents.

2. Identifying relationships

• Students use tools, technologies, and/or models to analyze the data and identify and describe* relationships in the datasets, including: 

  • The relationships between the changes in one system and changes in another (or within the same) Earth system; and 

  • Possible feedbacks, including one example of feedback to the climate. 

• Students analyze data to identify effects of human activity and specific technologies on Earth’s systems if present.

3. Interpreting data

• Students use the analyzed data to describe* a mechanism for the feedbacks between two of Earth’s systems and whether the feedback is positive or negative, increasing (destabilizing) or decreasing (stabilizing) the original changes.

• Students use the analyzed data to describe* a particular unanticipated or unintended effect of a selected technology on Earth’s systems if present.

• Students include a statement regarding how variation or uncertainty in the data (e.g., limitations, accuracy, any bias in the data resulting from choice of sample, scale, instrumentation, etc.) may affect the interpretation of the data.

Standard Name: HS-LS4-3 Biological Evolution: Unity and Diversity

Standard: Apply concepts of statistics and probability to support explanations that organisms with an advantageous heritable trait tend to increase in proportion to organisms lacking this trait.

Observable Features of Student Performance by the end of the Course: 

1. Organizing data 

• Students organize data (e.g., using tables, graphs and charts) by the distribution of genetic traits over time.

• Students describe* what each dataset represents

2. Identifying relationships 

• Students perform and use appropriate statistical analyses of data, including probability measures, to determine patterns of change in numerical distribution of traits over various time and population scales.

3. Interpreting data

• Students use the data analyses as evidence to support explanations about the following: 

  • • Positive or negative effects on survival and reproduction of individuals as relating to their expression of a variable trait in a population; 

    • Natural selection as the cause of increases and decreases in heritable traits over time in a population, but only if it affects reproductive success; and 

    • The changes in distribution of adaptations of anatomical, behavioral, and physiological traits in a population.

Standard Name: HS-LS3-3 Heredity: Inheritance and Variation of Traits

Standard: Apply concepts of statistics and probability to explain the variation and distribution of expressed traits in a population.

Observable Features of Student Performance by the end of the Course: 

1. Organizing data

• Students organize the given data by the frequency, distribution, and variation of expressed traits in the population.

2. Identifying relationships 

• Students perform and use appropriate statistical analyses of data, including probability measures, to determine the relationship between a trait’s occurrence within a population and environmental factors.

3. Interpreting data 

• Students analyze and interpret data to explain the distribution of expressed traits, including: 

  • • Recognition and use of patterns in the statistical analysis to predict changes in trait distribution within a population if environmental variables change; and 

    • Description* of the expression of a chosen trait and its variations as causative or correlational to some environmental factor based on reliable evidence.

Standard Name: HS-PS2-1 Motion and Stability: Forces and Interactions

Standard: Analyze data to support the claim that Newton’s second law of motion describes the mathematical relationship among the net force on a macroscopic object, its mass, and its acceleration.

Observable Features of Student Performance by the end of the Course: 

1. Organizing data

• Students organize data that represent the net force on a macroscopic object, its mass (which is held constant), and its acceleration (e.g., via tables, graphs, charts, vector drawings).

2. Identifying relationships 

• Students use tools, technologies, and/or models to analyze the data and identify relationships within the datasets, including: 

  • • A more massive object experiencing the same net force as a less massive object has a smaller acceleration, and a larger net force on a given object produces a correspondingly larger acceleration; and 

    • The result of gravitation is a constant acceleration on macroscopic objects as evidenced by the fact that the ratio of net force to mass remains constant. 

3. Interpreting data

• Students use the analyzed data as evidence to describe* that the relationship between the observed quantities is accurately modeled across the range of data by the formula a = Fnet/m (e.g., double force yields double acceleration, etc.).

• Students use the data as empirical evidence to distinguish between causal and correlational relationships linking force, mass, and acceleration.

• Students express the relationship Fnet=ma in terms of causality, namely that a net force on an object causes the object to accelerate.