Culturally Relevant Pedagogy in Teaching Data-Centric Courses

INFORMS–March 7th

Travis Weiland

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For Slides: bit.ly/INFORMS_CRP

Immanuel Williams

California Polytechnic State University, San Luis Obispo

GATO365 Learning Center

Audience

• Data mining
• Operations Research
• Management Science
• Data Science
• Mathematics and Statistics

Common Ground: We all care about making sense of data to make decisions that we hope lead to better futures.

Context is Central

• “Data are numbers in context” (Cobb & Moore, 1997)
• Context is an integral aspect of statistical enquiry (Wild & Pfannkuch, 1999)

• Data do not exist in a vacuum they are socially, culturally, and historically situated

Types of Questions we Typically ask in Class

• What are the three main types of machine learning?
• List the common evaluation metrics used for classification models.
• Name the typical steps in a data science project workflow.
• Explain the difference between supervised and unsupervised learning.
• Describe how a decision tree algorithm works in simple terms.
• Summarize the purpose of data preprocessing.
• How would you explain the concept of correlation to someone who is not familiar with statistics?
• Give an example of when you would use a bar chart versus a scatter plot.

Reflection

1. How much time do you spend explaining the context of a dataset or providing background information on the issue you are investigating?
2. How often do the tasks you ask your student to engage in require them to understand the context of a dataset or connect to students everyday lives or issues in society?
3. How much time do you spend finding or create datasets that would be relevant to your students?
4. How often have you had a student ask you when they will ever use what you are teaching them?

Culturally Relevant Pedagogy

“Culturally relevant pedagogy rests on three criteria or propositions:

1. students must experience academic success
2. students must develop and/or maintain cultural competence; and
3. students must develop a critical consciousness through which they challenge the status quo of the current social order.” (Ladson Billings’, 1995, p. 160)

Cultural Competence

• “Cultural competence focuses on the ability to successfully teach students who come from cultures other than our own. It entails developing certain personal and interpersonal awareness and sensitivities, developing certain bodies of cultural knowledge, and mastering a set of skills that, taken together, underlie effective cross-cultural teaching.” (Moule, 2012)
• Understanding one’s culture and being open to the ways and norms of other cultures

Culturally Responsive Teaching

• Similar to culturally relevant pedagogy there is also culturally responsive teaching
• Culture is at the center of both
• Gay (2010) describes culturally responsive teaching as “using the cultural knowledge, prior experiences, frames of reference, and performance styles of ethnically diverse students to make learning encounters more relevant to and effective for them” (p.31)
• Overall they are very similar except for one key difference and that is critical consciousness

Critical Consciousness

• Reading and writing the world (Freire, 1970)
• Interrogating taken for granted norms (Giroux, 1993)
• Transforming social structures for more just futures

Data Feminism

• Examine Power. Data feminism begins by analyzing how power operates in the world.
• Challenge Power. Data feminism commits to challenging unequal power structures and working toward justice.

• Elevate Emotion and Embodiment. Data feminism teaches us to value multiple forms of knowledge, including the knowledge that comes from people as living, feeling bodies in the world.
• Rethink Binaries and Hierarchies. Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression.

Data Feminism

• Embrace Pluralism. Data feminism insists that the most complete knowledge comes from synthesizing multiple perspectives, with priority given to local, Indigenous, and experiential ways of knowing.
• Consider Context. Data feminism asserts that data are not neutral or objective. They are the products of unequal social relations, and this context is essential for conducting accurate, ethical analysis.
• Make Labor Visible. The work of data science, like all work in the world, is the work of many hands. Data feminism makes this labor visible so that it can be recognized and valued.

Weapons of Math Destruction

• Big data algorithms are increasingly used in ways that reinforce pre-existing biases and perpetuate or even accentuate inequalities
• Use of big data and algorithms for decision making is problematic because they are opaque, unregulated, and difficult to contest.
• Additionally, they are easily scalable, thereby amplifying there negative impacts.

Relevance as a Starting Point

Cultural competence and critical consciousness are challenging and time consuming to develop, which can be daunting.

We see relevance as a starting point on that journey

Source: Open Colleges

What Counts as Culture

“The set of distinctive spiritual, material, intellectual and emotional features of society or a social group, that encompasses, not only art and literature, but lifestyles, ways of living together, value systems, traditions and beliefs.” (UNESCO, 2001)

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Data and Culture

Data is steeped in culture

• How were the variables/attributes operationalized?
• How was the data collected?
• Who or what was the data collected on? Who or what is absent from the data?
• Who analyzed the data and what decisions did they make in visualizing and summarizing the data?
• Who created the story to convey to others what they learned from the data?
• Who is the audience of the data story?

Incorporating Culture into Teaching

• Provide Windows and Mirrors “—a mirror in the sense of offering students a chance to see oneself; a window in the sense of being able to see a different view onto the world” (Gutiérrez, 2012, p.35)
• Inclusive design “the development and delivery of course materials should be inclusive of a diverse body of learners” (Dogucu et al., 2023, p.2)
• In summary: Who your students are matters and should drive instructional decision making

Group Affiliation/ Cultural Groups

• Race/ethnicity
• Sex, Gender Identity, & Sexual Orientation
• Class Identification
• Spatial/Place
• Interest Based Social Groups
• Work/Career
• Religion
• Age Group
• Political

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Important Note

• Our list makes it sound to some like everything counts as culture
• An important note is that it needs to be relevant to the actual students in your classroom
• Another approach is to find community or justice based issues to consider
• Need to do some self work and reading to support delving into controversial issues in the classroom

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Culturally Relevant Data (CRD)

Culturally relevant data are structured datasets (i.e., the classic definition that has rows that represent elements from a population and columns that denote information collected on the elements (Wickham and Grolemund 2016)) or entities that contain variables and statistics that are relevant to and considered in relation to 1) a concern that is relevant to students based on their communities or cultures they identify with or 2) an issue of social justice that impacts a student’s community or culture.

Example: Traffic Stop

Each row (observational unit) represent a single traffic stop.

Each column (variables) represents a characteristic of the stop including demographics of stopping officer and driver

Traffic stop data has historically shown disproportionality in application to drivers of different races

Source: Charlotte Open Data portal

Example: Traffic Stops

Traffic stops are an issue of social justice that impacts a student’s community as traffic stops are the most common interaction people have with police.

Example Traffic Stops

You can find similar data that may be more locally relevant to you and your students by looking at your local police departments website.

Another great source is the Stanford Open Policing Project

What is Relevant to your Students?

The first step in engaging in CRP is to get to know your students.

• Class survey
• Community walks
• Taking time to talk to your students before or after class
• Office hours

If you want to delve further into issues of justice

• Read the newspaper, what are important issue?
• Talk to community activist
• Talk to your colleagues in the social sciences

Finding Data

Where can you find data that works for your students and classes?

My first recommendation is governmental datasets that are publicly available

• Data.gov
• Census microdata from IPUMS
• Civil Rights Data Collection or NAEP for Educational Data
• USGS or NOAA for hydrology or climate data
• Local governmental agencies

As a Note on Federal Data

Access is changing

It is disappearing, being altered, or future data collection is being halted in cases across the Federal bureaucracy

Data.gov

Though the availability of data appears to be shrinking there is still a lot here to explore.

NOAA and USGS

USGS National Water Dashboard

NOAA Space Weather Dashboards

NOAA Climate tools and resources

NOAA Coastal Watch

National Weather Service

Census Micro Data IPUMS

IPUMS is a data warehouse and provider for census data around the world including the U.S. as well as other governmental datasets.

Civil Rights Data Collection

U.N. Sustainable Development Goals

U.N. Data Commons for SDGs holds data on the SDG indicators including data visualizations and raw data in Tidy format.

These help connect to issues of social justice and also international students

Finding Data

When governmental data doesn’t do the trick consider data journalists, NGOs, and think tanks who also provide open access data and metadata.

• ProPublica
• Fivethirtyeight
• New York Times
• Online Journalism Awards

There is also dataset search tools

• Kaggle
• Google Dataset Search

ProPublica is an independent, nonprofit newsroom that produces investigative journalism with moral force that use publicly available data and FOIA request to pull together data and create reports and visualizations

FiveThirtyEight

Lot’s of polling data

Used to do lots of sports and investigative journalism but seems to be folded under ABC News now.

New York Times

What’s Going on in this Graph is a great starting point for finding relevant data. Sometimes the data is easily linked, sometimes you have to go searching.

Kaggle

Kaggle is a search tool for datasets among other things

Crowd sourced quality control so you need to do some vetting of the data

Google Dataset Search

Connects to many other sources of data

Creating Data

This approach can make relevant datasets about issues that are not easy to find publicly available data on. However it is very difficult and time consuming so be prepared to plan ahead.

• Merging together pre-existing datasets to create new opportunities for exploring issues with new variables
• Processing pre-existing datasets to create new attributes or tame for easier use and consumption by your student audience
• Collecting new data as a part of the course
• Scraping data from online sources

Implementation Notes

• Plan for Mistakes: Prepare to address misunderstandings.
• Learn with Students: Be open to learning alongside students.
• Point out Limitations: There are limitations to data and it is important to be honest and upfront about those limitations.
• Listen: As faculty we are used to talking but to engage in CRP you need to also listen actively
• Context: Spend time to learn about the context yourself
• Agency: Create space for your students to have agency of the issues they explore or the datasets they use

Pitfalls

• Avoid essentializing groups of people or normalizing a particular group.
• Do not position students as representatives of their groups.
• Don’t rush unpacking the context of the data
• Don’t switch around contexts/issues all the time, find a few key ones and thread them through multiple assignments/units
• Don’t try to find a personalized relevant dataset for each individual student, focus instead on a few diverse datasets that connect to most student overall.
• Be aware of how discussing certain issues may negatively impact some students or bring up past traumas

Reflect

One of the most important practices for teaching is reflection

• Reflect on your efforts to get to know your students
• What did you learn about them?
• Which students did you learn about?
• Which students did you not get to know? How might you do better next time?
• Reflect on the relevance of the datasets you chose to your students
• How many students seemed to connect to the issues you presented?
• How did students react to or engage with the issues you presented data on? Was it positive, negative, or neutral?
• Reflect on student learning
• How did the datasets support student learning of your learning objectives?
• How did the datasets you used impact student engagement in class?

Additional Resources to Consider

Equitable and Engaging Mathematics Teaching: A Guide to Disrupting Hierarchies in the Classroom by Daniel Reinholz

Courageous Conversations Toolkit by Hannah Wilson

Managing Difficult Classroom Discussions by Indian University Bloomington

Resources for Difficult Dialogues in the Classroom by Michigan State University

Thank You

Thank you for coming

Please feel free to email anytime if you have questions or are looking for resources tweilan1@charlotte.edu

bit.ly/INFORMS_CRP