Supporting Teachers in Reading and Writing the World with Data

Travis Weiland, Caitlin Ireland, Constant Segbefia

Goals and Conjectures

Research Questions

• Research Question 1: How do secondary mathematics teachers develop a critical statistical literacy for doing and teaching statistics through a sustained PLC?  
• Research Question 2: How does the design of the PLC support secondary teachers’ development of a critical statistical literacy for doing and teaching statistics? 
• Research Question 3: How does the participation of secondary mathematics teachers in a sustained PLC, facilitate the transfer of their literacy developed while situated in a PLC, into the opportunities they create for their students to learn statistics situated in their classrooms?  

Critical Statistical Literacy

• Practices at the intersection of critical and statistical literacies
• “identify and interrogate social structures and discourses that shape and are reinforced by data-based arguments” (Weiland, 2017, p. 41).
• Places an emphasis on the role of individual’s subjectivity in carryout and interpreting data and data investigations, so that the individual can identify both personal and societal biases and work to balance those tensions.
• 10 main practices described for reading and writing the world (Freire, 1970) with statistics.

Change Theory

• A starting point for development is reflecting on tensions in a community/system (i.e., generative themes; Freire, 1970).
• Learning can then be seen through actions taken as a result of reflecting on such tensions (Lave & Wenger, 1991; Vygotsky, 1930).
• Change occurs through individuals being engaged in the practices of a community in cycles of reflection and action (i.e., praxis) to become attuned to new practices (Lave & Wenger, 1991)
• Design is meant to start structured more by research team and then move more of the agency in structuring to participants as they feel comfortable taking it up

Change Theory

Change Theory

Designed Generative Themes

• Representative-----Different
• Uncertainty----Certainty
• Signal----Noise

Issues in Data Investigations

1. National-U.S. House of Representatives
2. State/School-Racial bias in School OSS
3. Community-Food Access
4. Community/School-School Funding
5. National/State-Income Inequality

Central Ideas

• Local education standards
• GAISE II Framework (Bargagliotti et al., 2020);  
• Statistical Education of Teachers (Franklin et al., 2015);  
• Developing Essential Understandings of Statistics (Kader & Jacobbe, 2013; Peck et al., 2013);  
• Teaching Tolerance Anti-bias Framework
• Contextual knowledge of the issue we are investigating

Culture & Norms

• We considered socio-statistical norms proposed by Ben-Zvi et al. (2019) building from Yackel & Cobb’s (1996) Socio-mathematical norms
• We focused on:
• Claims should be supported with data
• Position yourself in your data arguments/stories and consider how you are positioned by others in their data arguments/stories
• Consider multiple interpretations of data
• Be transparent about and document your data practices/moves
• Always consider the context of the data
• Data points and data aggregates are both meaningful and help tell important stories

Culture/Norms

Pedagogy

Goal is move toward critical pedagogy

Rubel’s (2017) work describe equity-driven mathematics instruction as including:

• Standards-based
• Complex instruction
• Culturally relevant pedagogy
• Teaching mathematics for social justice

Berry et al. (2020) describe a developmental trajectory

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Authentic Practice: Data Investigation Process

• Further reading: Thinking Through the Data Investigation Process
• Further resources: InStep with Data InSTEP | NC State University (instepwithdata.org)

Critical Statistical

Literacy

Technology Tools

CODAP

• Free
• Web-based
• Dynamic
• Designed based on learning sciences research

Design Principles/Embodiments

• Data investigations of relevant issues are at the core of authentic practice of critical statistical literacy 
• Authentic practice involves engaging in ongoing cycles of reflection and action 
• Real data and appropriate technology tools must be a part of the design of data investigations for authentic practice 
• Community building is an explicit aspect of the design 
• Pedagogy is modeled and made explicit 
• Understanding of content and context are both valued in the development of central ideas in data investigations
• The design is transparent and explicitly communicated to participants/teachers 
• The relevance of topics is in the eyes of the beholder and should be considered at different levels (I.e. international, national, community, local, home) in relation to dialectic tensions (difference---representation; certain---uncertain; signal---noise).

Intervention Overview:

Professional Learning Community

Participants

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• 18 teachers total
• Year 1: 8
• Year 2: 17 (7 from Year 1)
• Year 3: 16 (6 from Year 1)
• Gained 10 teachers during Year 2 (All remain)
• Two districts (only 1 still has teachers actively participating)

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Overview

• Met for 1-2 weeks for three summers, 6 hours per day and lunch.
• District math specialist attended parts.
• Virtual and In-person meetings scattered during the school year for continuation and to support translation into the classroom
• Started heavily scaffolded with issues coming from the research team and then shift more to issues from the teacher’s students and space and time to plan and create guided by their goals

Year 1

Summer 1 PLC (2023)

• Focus heavily on reading the word and the world with data visualizations
• Introduced using technology for data investigations
• Writing the word through data investigation elements and connections
• Creating relevant activities
• Exploratory data analysis
• Simulation based Inference

School Year 1 PLC (2023-2024)

• Focus on translating ideas from summer into classroom
• Creating and using data visualization activities in the classroom

Year 2

Summer 2 PLC (2024)

• Focus heavily on writing the word and the world.
• Connecting context and content and giving them equal consideration
• Spatial data analysis to connect to place
• Choosing and creating a data investigation around a relevant issue (i.e. food access)

School Year 2 PLC (2024-2025) 

• Focus on translating ideas from summer into classroom

Year 3

Summer 3 PLC (2025)

• Focus heavily on writing the word and the world through data argumentation
• Connecting claims, evidence and reasoning to data investigative process
• Developing lesson plans for use in classroom

School Year 3 PLC (2025-2026)

• Focus on translating ideas from summer into classroom
• Use of structured lesson plan
• Connecting learning to policy changes
• Attend and present ideas at state mathematics teacher conference

Year 4

Summer 4 PLC (2026)

• Developing lesson plans for use in classroom
• Brainstorming ideas for sustainability after funding ends

School Year 4 PLC (2026-2027)

• Focus on translating ideas from summer into classroom
• Use of structured lesson plan
• Connecting learning to policy changes
• Attend and present ideas at state mathematics teacher conference
• Taking up leadership roles in statistics education in their district/state

Reading Data Visualization Activity

What do you notice?

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What do you wonder?

Going Beyond Notice and Wonder

There are different ways you can read a data visualization

• Reading the data
• Reading between the data
• Reading beyond the data
• Reading behind the data

Reading

the Data

• What data are displayed in this visualization?
• What is measured in this graph?
• How does this data visualization make you feel?

Reading the Data

• Locating and extracting relevant information from data visualization
• Involves
• Recognizing the components of data visualizations, the interrelationships among these components, and the effect of these components on the presentation of information in data visualizations
• Speaking the language of specific data visualizations when reasoning about information displayed in graphical form
• Looking for oneself in the data

Reading Between

the Data

• What patterns or trends do you see in the data?
• What relationship(s) is the author highlighting?
• How do the relationships displayed here compare to your own experiences?
• Why do you think the author chose to highlight this relationship?

Reading Between the Data

• Find patterns or relationships in the data visualization
• Involves
• Understanding relationships among tables, graphs, and data
• Making sense of a graph, but avoiding personalization and maintaining an objective stance while talking about the graphs
• Recognizing the types of relationships (correlational or causal) can be claimed based on the data collection methods
• Making sense of the data visualization in relation to your personal experiences
• Identifying and questioning how the author has highlighted particular relationships in the graph
• Reimagining other ways that relationships could be highlighted or visualized

Reading Beyond

the Data

• What story is the author telling with this data visualization?
• What is a claim you could make from this data visualization?
• What evidence are you using to make this claim?

Reading Beyond the Data

• Move beyond the data visualization to making predictions or inferences, answering a question
• Involves
• Interpreting information in a graph and answering questions about it
• Predicting outcomes based on reasonable claims made from the graph
• Recognition of One’s Own Sociopolitical/ Critical Consciousness
• Acknowledging Alternate Explanations
• Recognizing the story the author is trying to tell with this data
• Questioning the author’s motives for telling this story
• Identifying the inequities in the interpretation of the data visualization
• Understanding one’s social location, subjectivity, political context and having a sociohistorical and political knowledge of self and understanding how it influences one’s interpretation of information.

Reading Behind

the Data

• How do you think the data were collected?
• What other information would help you understand this issue?
• What other information would help you understand the ways this data impacts you and your community?

Reading Behind the Data

• Making connections between the context and the data visualization including how the context of how that data was collected and represented and how those aspects shape our view of the context
• Involves
• Looking for possible causes of variation based on the context being measured and the way the data was collected
• Recognizing appropriate graphs for a given data set and its context
• Questioning sample size and methods and their impacts on inferences (i.e. practical significance vs. statistical significant; effect vs. no effect)
• Recognizing when common sources of bias are present in the data collection
• Recognizing appropriate statistics & appropriate representations
• Recognizing and questioning the source of the data including what is quantified and how it was measured

Reading Behind the Data

• Involves
• Using your knowledge of the context of the data to interpret why particular patterns exist in data as well as data generation process (e.g., knowledge of frost in the country through the news)
• Using knowledge of one’s community to interpret why particular patterns exist in the data to question aspects of the data generations process (e.g., knowledge of frost in community)
• Questioning the investigative process undertaken based on personal experiences/identity
• Reimaging the data visualization by considering other ways reality could be quantified and/or collected
• Recognition of the gaps in one’s knowledge [of the context] needed to interpret the statistical message

Reflection

Turn and talk with a neighbor

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• How did you read the graph differently based on the questions that were asked?
• How could you use this in your teaching?

How can we do this for our classes?

Step 1: Consider your Learning Objective

• What are your learning objectives for the day?
• How will this activity help you support your students in achieving those objectives?
• Is learning to read data visualizations the main objective?
• Are you using a DV activity to launch an investigation where your goals for the task are to get students engaged in the topic you will be investigating?
• Is the DV activity meant to be a review of concepts students have learned previously or serve as an exit ticket to assess student learning?

Step 2: Select a Data Visualization

• Disclaimer: This step can be deceptively time consuming and challenging

Levels of Relevance

Where to Find Data Visualizations

• Great starting point is google
• Media, in particular, for really engaging ones try New York Times or Washington Post.
• Local news
• Community organizations
• Governmental organizations

Step 3: Select the data visualization practices you want to focus on.

• What types of reading do you want your students to focus on and how does that connect to your standards and learning objectives?
• We have a checklist on our how-to document that is helpful to think this through

Step 4: Choose purposeful questions to guide the reading data visualization discussion.

• What types of reading practices are you aiming for?
• What is in the data visualization?
• What are your learning objectives?
• What questions will meet your students where they are?

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• We have a list on our how-to document that is helpful to get started

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Step 4: Choose purposeful questions to guide the reading data visualization discussion.

Step 5: Choose how you will teach the data visualization activity

• Quick Launch/warm up
• Launch into full blown data investigation
• Exit ticket
• Stations

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• Let’s take a look at some possible approaches

Slow Reveal Graph

• Exposes students gradually to the different components of data visualization
• Students are asked to consider what the visualization is showing them before being given more features of the graph
• Each slide deck begins with a naked visualization with a notice/wonder prompt
• Each subsequent slide adds different components of the visualization along with increasingly complex questions to invite discourse amongst students.
• https://slowrevealgraphs.com/

• What do you notice?

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• What do you wonder?

• What new information did we just learn?

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• How does that change your thinking?

• Now what information do we have?
• How does the information about the x-axis change your interpretation?
• What are your predictions?
• What other information would you like to know?

https://www.roymorgan.com/findings/wealth-inequality-in-australia-is-getting-worse

• What story is the author trying to tell?
• How does this make you feel?
• How does this relate to you or your community?

Reflection

Turn and talk with a neighbor

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• How did this activity make you think differently about reading and creating data visualizations?
• How could you use this in your teaching?
• How could this impact your research?

Morning Tea

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10:30 - 11:00

Data Visualization as a Launch into Data Investigations

• Thinking about data visualization as a starting point to a data investigation
• Let’s explore a tool where you can look at lots of different data visualizations and then dive into a data investigation.
• Click this link CODAP Workspace

Data Investigation Process Activity

https://www.abc.net.au/news/2022-06-27/wgea-report/101186358?utm_source=abc_news_web&utm_medium=content_shared&utm_campaign=abc_news_web&utm_content=link

Authentic Practice: Data Investigation Process

• Further reading: Thinking Through the Data Investigation Process
• Further resources: InStep with Data InSTEP | NC State University (instepwithdata.org)

Argumentation Process

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Data argumentation is part of the data investigative process, but is a part of the process that has not been commonly focused on in teaching and is the part of the process that other people generally interact with and learn from making it especially important.

To highlight the argumentation process we have chosen to depict it as an embedded process that merits individual attention.

Argumentation

Process

Argumentation Process

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The argumentation process is a part of the entire investigative process.

However there are two components of the data investigation process where the argumentation process is particularly important and often under emphasized in teaching. Those are during framing the problem and communicate and proposed action.

Frame the Problem

Data investigations are driven by the desire to explore an issue/problem. Therefore a first step is often to try and frame the problem you want to investigate. This starts informally as just trying to put into words an issue you have noticed.

• You could start by stating a conjecture.
• A conjecture is an opinion or conviction formed on the basis of anecdotes, guesswork, or intuition.
• In other words, it is a speculation, or an educated guess that is believed to be true based on lived experience, but lacks formal proof or substantial evidence.

Conjecture Examples

It appears that when students use flashcards for vocabulary, their test scores tend to be higher.

My observation suggests that students who actively participate in school clubs are less likely to experience academic stress.

Based on the patterns I've seen in our class, I'd conjecture that the average time spent on homework per night is around 1.5 to 2 hours for most students freshman year and increases each year and as classes get more difficult.

It seems plausible that if we start school later in the morning, then students would be happier and better able to pay attention.

I'm inclined to believe that students who teach a concept to someone else retain that information better themselves.

Frame the Problem

• From a conjecture you then need to frame the problem into a question that you are trying to answer with your data investigation.
• Research questions often start very broad and might just be your conjecture rephrased as a question.
• From a broad research question you need to narrow it into an investigative question that is easily answerable with data that we can collect or find and analyze in a relatively short period of time (i.e. 2-5 class periods).
• A final key element is a hypothesis. A hypothesis is a possible explanation or educated guess about a phenomenon that can be tested through investigation.

Example: Putting it all together

Conjecture

Research Question

Investigative Question

Hypothesis

It seems plausible that if we start school later in the morning, then students would be happier and better able to pay attention.

I hypothesize that mean self-reported attentiveness and mood of high school students will increase significantly from a normal start time to starting school one hour later.

How would student mood and attentiveness change if school started later?

How does mean self-reported attentiveness and mood of high school students change from a normal start time to starting school one hour later?

Problem/Issue: Many high school students struggle with sleep deprivation, which is believed to negatively impact their attentiveness and mood during early morning classes. However, the direct impact of later school start times on these specific student outcomes in a high school setting has not been sufficiently quantified to inform policy decisions.

Frame the Problem

Research Question

Conjecture

On Your Own

On Your Own

A research question is focused on a problem/issue that is framed for open-ended inquiry that guides a research study. In other words, it's a question that the research aims to answer, providing the starting point and guiding the entire research process. This is generally a large and broad question that would require multiple analyzes to begin to answer. It serves as a starting point to then refine to specific investigative questions

A conjecture is an opinion or conviction formed on the basis of anecdotes, guesswork, or intuition. In other words it is a speculation, or an educated guess that is believed to be true based on lived experience, but lacks formal proof or substantial evidence. Creating conjectures helps to write out what you think you will find based on your own lived experiences.

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Write a conjecture(s) you have related to the problem/issue you identified?

Write a research question you have related to the problem/issue you identified?

Idea comparison

Framing the Problem-OYO

To help guide your process we have created a template in our shared folder.

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Group 1

Group 2

Group 3

Example

We are going to go through a lesson I have created a detailed plan for that you can find here.

Consider and Collect Data

• We are going to use the Census Microdata Plug-in in CODAP
• While you are exploring think of this: "What is a question that would be interesting to investigate with this data?"

Get Data for Your Country

https://international.ipums.org/international/

Getting Started in CODAP

• Go to CODAP 
• Click the Plugins Icon in the menu in the top left-hand corner and then select Getting Data and then select Microdata Portal. 

Selecting Data

With this plugins you can choose what locations you want to draw samples from in terms of location (i.e. States in the U.S.) and time point (i.e. year) as well as the attributes to include, and the size of the sample  

Selecting Attributes

There are many attributes to choose from nested in 6 main groups including:

• Basic Demographics
• Race, Ancestry, Origins
• Work & Employment
• Income
• Geography
• Other

You can view the attributes in each group by clicking on the down arrow next to each group

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Selecting Attributes

I will walk through how to select attributes selecting a small subset but feel free to add in any you like.

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If it is easier for some I also created a workspace with all the attributes selected already that we can use together if you want to work with the same dataset.

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Meta-Data

• There is data about the data available if you click on the   icon in the top right-hand corner of the plugin 
• Resources include the original questionnaire, a codebook, and a reference for using the plugin in CODAP

Structure of Data

• The data comes in a hierarchical form with individuals nested into states
• Rows in the people section represent individuals who completed the 2020 census.
• Columns represent the measures/attributes

Visualize

• CODAP creates a number of different data visualizations based on what type of variables you are visualizing and how many variables you are trying to visualize at the same time. 
• To create data visualization, you drag and drop attributes (variable) to different locations on a graph including the x-axis, y-axis, center of the graph (after at least one axis has an attribute), the top heading (after at least one axis has an attribute), or the right heading (after at least one axis has an attribute) 

Consider, Explore, and Visualize

Let’s take a look at the data. The questions below will help focus your attention to important aspects of the data to consider for your investigation.

1. What are the cases/observational units?
2. What variables are present in this data set?
3. How do you think they were measured?
4. What questions do you have about the data?
5. What questions would you like to investigate with the data?
6. What is the structure of your data?
7. Do you need to create, modify, or filter any of the attributes/variables?

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What do we do about this?

Consider Metadata

Filter

Filter

Filter

Tools to Help Guide the Process

Worksheet for Consider, Process, Explore, Visualize and Model Data

Data Investigations Pathways

Data Investigation Briefs

• 1 Cat
• 1 Quant
• 1 Cat/1 Quant
• 2 Cat
• 2 Quant

Data Moves

• Overview
• Calculating/Recording
• Filtering
• Grouping
• Joining
• Summarizing

We have developed resources and tools to support teachers in learning about data investigations and the argumentation process as well as to help them do it with their students. We designed many of these tools to be used with teacher or student audiences.

Writing an Investigative Question

Research questions are a great starting point but they are often larger than we can easily answer in a short period of time and would require multiple analyses to answer. To help break the work up we are next going to narrow down to an investigative question.

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An investigative question is specific and answerable and serves to guide a well-bounded data investigation that can be conducted with available resources in class today and tomorrow.

Criterion for Investigative Questions

• The variable(s) of interest is/are clear and available
• Report what variables are present in the question
• Are those variables in the dataset?
• The population of interest is clear
• What is the population in the question?
• The intent is clear
• Is it clear what the intent of the question is?
• What is the question getting at?
• Variability is present/expected in the data
• Is there more than one possible outcome for the variables being investigated?
• The question can be answered with the data
• Is it possible to answer the question as it is intended with the data we have available to us?
• The question is one that is worth investigating, that is interesting, and has a purpose
• Why is this question worth investigating? So what?
• The question allows for analysis to be made of a whole group
• Is the question focused on overall trends and patterns?
• Does the question just ask for a descriptive statistic? If yes, it is not a statistical question.

Writing Questions

In your group, use what you learned from considering the census data to now write a good investigative question

Framing the Problem-OYO

To help guide your process we have created a template in our shared folder.

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Group 1

Group 2

Group 3

Share

Now that you have questions, the best way to see if they make sense is to have someone else review them and provide constructive feedback.

Pair up with someone and review their investigative question.

Does it match the criterion?

Provide constructive feedback.

Reflect and Refine

Now go back to your own question and review the feedback.

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Do you have any question on the feedback to ask?

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Refine your Question based on the feedback.

Hypotheses

A hypothesis is a possible explanation or educated guess about a phenomenon that can be tested through investigation. In other words it is your prediction about what might happen, based on your understanding.

Key characteristics of hypotheses

Tentative: It's a proposed explanation that hasn't been proven yet.

Testable: It can be put to the test through experiments, observations, or other research methods.

Specific: It clearly states what the researcher expects to find or observe. This should include some detail on the measures that will be used and statistics that will be relied on in the analysis.

Based on existing knowledge: It should be informed by previous research and observations, not just a random guess.

Addresses a research question: It provides a clear answer or prediction to the question being investigated.

Create Your Own

Now go back and write a hypothesis for your investigative question.

Framing the Problem-OYO

To help guide your process we have created a template in our shared folder.

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Group 1

Group 2

Group 3

Visualize Data

Explore Data

In a Group

In a Group

A picture says a thousand words. Start with a picture of your data. Play around with different types of data visualizations and see what new aspects of the data you are able to observe. Consider processing the data in different ways in conjunction with visualizing it. Try overlaying different types of visuals. Consider what visualizations of the data create the best evidence for your question.

Exploring the data can take many forms. In general it is important to collect summary statistics for the variables you are considering for your investigative question. This may also involving going back and forth between exploring the dating and processing the data in conjunction with visualizing the data.

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FInd the descriptive statistics relative to the variables you are investigating and the question you have posed

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Play with different visualizations of the variables you are exploring for your investigative question

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Idea comparison

Visualize

Visualize

Visualize

Visualize

Descriptive Statistics

Model

1. What statistics best represent the data?
2. How would you describe the data distributions?
3. How could you model the data to help investigate the investigative question?

Models

1. How do these visualizations show modeling the data in different ways?
2. How does this compare to what you did?

Share for Feedback

Share your model with one other group

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Does the model fit the question?

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Does the model fit the data?

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Does the model help explain the data?

Models and Hypotheses

How does your model compare to your hypothesis?

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If your model confirms your hypothesis look over it again and consider if there may be other models that might also be appropriate and perhap beter

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If your model refutes your hypothesis consider why and what that means in the context.

Communicate and Propose Action

After carrying out a data investigation it is important to communicate what you have learned from this process and relate it back to your original framing of the problem you are investigating. Some call this a data story as data does not speak for itself; people create stories with data. Others refer to this as a principled argument.

We choose to focus on principled arguments as the “principled” part of that name implies an argument that follows certain agreed upon norms or chains of reasoning.

A chain of reasoning is a multi-step explanation where each step logically leads to the next, such that someone else can follow them to the same final conclusion.

Lunch

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12:30 - 1:15

Making a Claim

After exploring, visualizing, and modeling with data, our next step is often to make a claim(s) related to the investigative question you have been investigating. A claim should be a concise and specific statement that is debatable and can be supported with data.

Concise: It should be 1-2 sentence at most

Specific: It should narrow down to a specific point, rather than being overly broad.

Debatable: Your claim should not merely be a statement of fact or summary – you need to take a position based on your analysis

Evidence

Claims cannot stand on their own in a principled argument, they must come with evidence and reasoning. In statistics there are many types of evidence to provide including:

• descriptive statistics
• data visualizations
• measurement considerations
• descriptions of sample and sampling
• a point of comparison
• and depending on the situation, inferential statistics.

To help make sense of how to use evidence we have created a rubric in this document

Reasoning

You cannot simply provide evidence though. To create an argument you must also provide reasons for why that evidence justifies the claim that you have made. The reasoning is like the glue that holds it all together. In the end you combine claims and evidence through a chain a reasoning such that someone else can follow your think to come to the same conclusions you have.

We have also created a rubric for considering the reasoning in an argument in this document.

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Communicate and Propose Action

Connecting the Pieces

Let’s start with some initial argument development to try identify and connect the basic pieces of the argument. For evidence in your final argument you will want to provide select data visualizations and statistics but for now you can just take notes as to what those things will be and focus on connecting the piece of evidence to the claim with reasoning.

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This document may serve as a helpful reference for this activity.

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This organizer can also help you arrange your argument.

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Connecting the Pieces

This organizer can also help you arrange your argument. Find it in our shared folder.

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Group 1

Group 2

Group 3

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Share

Now that you have a rough outline of a chain of reasoning the best way to see if it make sense is to have someone else review it and provide constructive feedback.

You all have access to each others documents.

Pair up with another group and review their argument organizer

Does it match the criterion?

Provide constructive feedback.

Reflect and Refine

Now go back to your own argument organizer and review the feedback.

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Do you have any question on the feedback to ask?

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Refine your argument based on the feedback.

Elements of a Principled Argument

• A statement of the problem/issue you are discussing. This may include things like:
• Research questions
• Conjectures
• Hypotheses
• Important contextual information or relevant literature
• Overview of Methods
• What is your data source?
• What is your sample and sample size?
• How did you process the data?
• How did you explore and visualize the data?
• How did you model the data?
• Claim(s)
• Evidence
• Reasoning
• Action/Decisions.
• What do we do based on this information?
• What actions do you recommend?

Propose Action

Generally an argument does not end with merely supporting a claim it usually goes into what we often call the “So What.” So what do we do know based on what we have learned? How does this help us make decisions? What actions are recommended based on these results? This is different for every question and issue and will draw upon your know of the issue you are investigating not just the results of your investigation.

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Argument Presentation

Now that you have learned about the various components of the Data Investigation Process and Data Argumentation, let’s spend time creating your own data argument. You will present your arguments to the workshop. Find these in our shared folder.

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Group 1

Group 2

Group 3

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Present

Now we will share our arguments with the community.

Feedback with Rubrics

Now we will practice giving feedback on your reasoning with our rubrics. Spend time looking over another participant’s presentation and use the rubric to give constructive feedback.

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Group 1

Group 2

Group 3

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Reflection

How has this helped you be critical of data arguments?

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Has this changed or shaped your perception of using data argumentation in the classroom?

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What do you find beneficial?

Website

We have a website live now with all the refined resources you have helped us to develop.

www.criticalstatisticalliteracy.org

Questions?

CriticalStatisticalLiteracy.org

Caitlin Ireland: csmit499@charlotte.edu

Travis Weiland: tweilan1@charlotte.edu

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How might you use this in your class?

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How does this framing make you think differently?

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What was helpful?

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