Computational Thinking
in Teacher Education

A Module on Data Analysis

Nadia Kennedy, Mathematics, City Tech

Boyan Kostadinov, Mathematics, City Tech

Ariane Masuda, Mathematics, City Tech

8/11/2022

Introduction

Course: MEDU 3003: Microteaching

Intermediate field school-based experience, which includes preparation for lesson and unit planning, student assessment and delivery of instruction.

Rationale:

This computing integration module focuses on engaging prospective mathematics teachers in a practice of using data sets to answer questions about local Brooklyn schools and learn more about the schools and students where they do field experience, and supporting them in developing data analytical and critical thinking skills.

For the purposes of these activities, we will use datasets from:

Learning Standards (LS)

  • 1.2. Digital Citizen Students recognize the rights, responsibilities and opportunities of living, learning and working in an interconnected digital world, and they act and model in ways that are safe, legal and ethical.

  • 1.3. Knowledge Constructor Students critically curate a variety of resources using digital tools to construct knowledge, produce creative artifacts and make meaningful learning experiences for themselves and others.

  • 1.7 Global Collaborator Students use digital tools to broaden their perspectives and enrich their learning by collaborating with others and working effectively in teams locally and globally.

Impact of Computing

  • 9-12.IC.1 Evaluate the impact of computing technologies on equity, access, and influence in a global society.
  • 7-8.IC.1 Compare and contrast tradeoffs associated with computing technologies that affect individuals and society.
  • 7-8.IC.3 Identify and discuss issues of ethics surrounding computing technologies and current events.
  • 9-12.IC.3 (Ethics) Debate issues of ethics related to real world computing technologies.
  • 7-8.IC.5 (Ethics) Analyze potential sources of bias that could be introduced to complex computer systems and the potential impact of these biases on individuals.

Computational Thinking

  • 9-12.CT.3 Refine and visualize complex data sets to tell different stories with the same data set.
  • 9-12.CT.7 Design or remix a program that utilizes a data structure to maintain changes to related pieces of data.

Digital Literacy

  • 9-12.DL.2 Communicate and work collaboratively with others using digital tools to support individual learning and contribute to the learning of others.

Standard 1: Candidate/Completer Performance

  • 1a. Content, pedagogical, and/or professional knowledge relevant to the credential or degree sought.
  • 1c. Culturally responsive practice, including intersectionality of race, ethnicity, class, gender identity and expression, sexual identity, and the impact of language acquisition and literacy development on learning.
  • 1d. Assessment of and for student learning, assessment and data literacy, and use of data to inform practice.

Standard 2: Completer Professional Competence and Growth

  • 2a. Understand and engage local school and cultural communities, and communicate and foster relationships with families/guardians/caregivers in a variety of communities.

Standard 3: Quality Program Practices

  • 3b. Develops and implements quality clinical experiences, where appropriate, in the context of documented and effective partnerships with P-12 schools and districts.
  • 3e. Engages in continuous improvement of programs and program components, and investigates opportunities for innovation, through an effective quality assurance system.

Learning Goals

The module is designed to:

  • A. Support teacher learning:

    • Teacher candidates articulate a question about a school’s student population and find, analyze and interpret data to answer the question.
    • Teacher candidates explore data sources, interpreting them to find patterns, groups, and visualizing interpretations to communicate findings.
    • Teacher candidates discuss the data that is already collected about schools and students and explore relationships between variables (i.e. test scores, demographics, grades, etc.).
    • Use CT to remix code for analyzing a dataset.
  • B. To integrate into teacher pedagogy:

    • Teacher candidates explore NYC open data base to compare and contrast schools and educational services in different parts of the city.
    • Describe a school profile by analyzing and visualizing data from an education data set.
    • In their lesson/unit planning, teacher candidates demonstrate that they have used student, and school performance data.
    • Teacher candidates develop a short presentation with graphs about their school and students to present to their peers.
  • C. To help teacher candidates develop awareness of ethical issues related to digital use and literacy:

    • Talk back to data – how it’s used, the assumptions made in its collection and visualization.
    • Understand the uses and limitations of big data sets / how they are collected, what’s missing.
    • Teacher candidates discuss the limitations of data, specifically what details of a community or student cannot be captured by data.

Activity 1 Warm-Up: Visualize Data

Learn about Your Field Experience School
and School District

Field Experience School and School District

To get started: Scroll down to the interactive map of the United States in the Miseducation (ProPublica) database and then answer the following questions:

  1. Click the tabs “Opportunity,” “Discipline,” “Segregation” and “Achievement Gap” and answer these two questions: What do you notice? What do you wonder about?

  2. Next, click the tabs “Black” and “Hispanic.” What do you notice? What do you wonder?

  3. Search for your school or district in the database. What do you notice in the results? What questions do you have?

Prepare a short report on what you have found about your school and about your school district. In your reflection, include detailed results, e.g., “School district ….’s composition is 24 % Black, 41%, Hispanic, 16% White, 17% Asian, 1% Native American, 1% Two or more races.” Or “White students are 1.7 times more likely to be enrolled in at least one AP class as Black students.” Be ready to share your findings in class. In-Class Gallery Walk Teacher candidates share their findings.

  • What did you find about your school/school district?
  • Do all students in NYC receive the same quality of education?
  • Do all students in America receive the same quality of education?
  • Do you think that there is a correlation between students’ race and the quality of education they receive?
  • What is the purpose of public education?
  • Is receiving a quality public education a right (for everyone) or a privilege (for some)?

Activity 2: (In)equitable School Funding

Investigate relationships between school funding, inequity, and achievement.

(In)equitable School Funding Investigate relationships between school funding, inequity, and achievement.

Choose one of the following ideas (or generate another) to investigate the interrelationship among school segregation, funding and inequality.

  1. Research your local school district budget, using public records or local media, such as newspapers or television reporting. What is the budget per student? How does that budget compare with the state average? The national average?

  2. Compare your findings about your local school budget to your research about segregation and student outcomes, using the Miseducation database. Do the results of your research suggest any correlations?

  3. Represent digitally? HOW? IDEAS? SHARE HOW? IDEAS?

  1. How much less total funding do school districts that serve predominantly students of color receive compared to school districts that serve predominantly white students?

  2. Why are school district borders problematic?

  3. How many of the nation’s schoolchildren are in “racially concentrated districts, where over 75 percent of students are either white or nonwhite”?

  4. How much less money, on average, do nonwhite districts receive than white districts?

  5. How are school districts funded?

  6. How does lack of school funding affect classrooms?

  7. What is fair school funding and why does it matter?

Activity 3: Segregation and Educational (In)equities

Investigate the relationship between segregation,
educational opportunities, and (in)equities

  1. Only a tiny number of black students are admitted to the highly selective public high schools in New York City (e.g., 2019, 2020, 2021) raising the pressure on officials to confront the decades-old challenge of integrating New York’s elite public schools. To learn more about this story, listen to this episode of The Daily. For more information, read this essay offering different perspectives on the problem and possible solutions. Make a case for what should be done — or not done — to make New York’s elite public schools more diverse.

  2. Pose a question in relation to NYC school segregation and inequities that you would like to answer. Use NYC Open database https://opendata.cityofnewyork.us/ to find an answer to your question. Be ready to share in class your question, results of your research and visual representations.

Gallery Walk: Share your findings.

  • How and why are schools still segregated in 2022 (in NYC, America and the world)?
  • What repercussions do segregated schools have for students and society?
  • What are potential remedies to address school segregation?

Activity 4: Using Data Analytics

Discussion Questions:

  1. What is bias?

  2. Could data be biased? Explain.

  3. If so, what might be some sources of bias?

  4. What can introduce bias in data representation and interpretation?

  5. What can guard against bias?

  6. In what ways data analytical tools can be helpful?

  7. Are there ways in which data analytical tools can be harmful?

  8. What is the impact of data technologies on society and individuals?

Activity 4: Using R for Data Analysis

From the verbs of plain English to the verbs of data analysis

Take your data, then filter by borough being "MANHATTAN", then select variables (columns) name, borough and mathprof, then arrange in descending (desc) order of mathprof, then slice the data through the first 10 rows (1:10), then print (kable) the resulting data into a table. Replace “then” above with the composition operator (|>) and note how the plain English instruction translates almost literary into the verbs of data analysis needed to implement this instruction.

data |> 
  filter(borough == "MANHATTAN") |> 
  select(c(name,borough,mathprof)) |> 
  arrange(desc(mathprof)) |> 
  slice(1:10) |> 
  kable()

Activity 4: Using R for Data Analysis

name borough mathprof
Special Music School MANHATTAN 98
East Side Middle School MANHATTAN 97
The Anderson School MANHATTAN 97
Tag Young Scholars MANHATTAN 96
New Explorations into Science, Technology & Math MANHATTAN 95
New York City Lab Middle School for Collaborative Studies MANHATTAN 94
The Clinton School MANHATTAN 93
M.S. 255 Salk School of Science MANHATTAN 92
M.S. 243 Center School MANHATTAN 90
Columbia Secondary School MANHATTAN 88

Activity 4: Using R for Data Analysis

data |> 
  group_by(district) |> 
  summarize(med_mathprof = median(mathprof, na.rm=TRUE)) |> 
  bar_chart(x=district, y=med_mathprof)

Designing for Equity

  • What moves did you make to try to support the needs of your learners, as well as broader issues of diversity, inclusion, justice?

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