Computing-Integrated Teacher Education at the City University of New York

Data Practices For Learning About Communities and Learners

This example comes with a design toolkit! Click here to access it!

The Premise

This computing integration focuses on supporting teacher candidates to build their confidence as decision-makers by developing a practice of leveraging data about the students, schools, and communities they serve. For the purposes of this toolkit, we will focus on non-assessment data such as self-reported student data, school data, or community open data.


Teacher candidates are preparing to enter complex social systems as the primary agents of change. The must balance their students’ and their own daily needs with the work of building relationships with colleagues, administration, communities, and families. By helping teacher candidates build a data practice, teacher education programs can give them a powerful tool in understanding, serving, and gaining the trust of the stakeholders in their future school communities. Data practices may include generating, collecting, finding data sources, interpreting them to find patterns, groups, or outliers, and visualizing interpretations to communicate findings. This toolkit will provide guidance on data practices for teacher by addressing questions such as: What should be part of a teacher’s data practice? What data is relevant to a teacher? What skills do they need to interpret and visualize data? How can this time and energy serve their instructional goals?

Courses that would lend themselves to this integration

  • Foundations – Childhood, Early Childhood, Secondary
  • Education Technology
  • Disciplinary Methods courses
  • Student Teaching Seminar

Potential Conversations and Activities

Teaching in this area could support teacher candidates to teach and learn…

About computing/tech
  • To support Teacher Learning
    • Defining what counts as ‘data’
    • ​​Understand mechanisms for data collection and types of data (textual, graphic, numeric, qualitative, quantitative)
    • Teacher candidates discuss the data that is already collected about their students and build a portrait of their student using only variables (i.e test scores, demographics, grades, ELL/SWD status, etc.)
    • Teacher candidates imagine sources of data about schools or communities surrounding schools that could help them better understand students and inform their instruction
    • Teacher candidates identify aspects of communities they’d like to learn more about, find data sources or collect data from communities, and tell a story (possibly through digital storytelling)
    • Teacher candidates keep a structured journal of their field experience and analyze entries for patterns
  • To integrate into Teacher Pedagogy
    • Teacher candidates ask students to list their interests on Post-Its and then group Post-Its by similar topics/themes
    • Teacher candidates ask students to map their trip to school identifying things like transportation modes, routine stops, and landmarks as well as asking them to personalize the map (metadata)
With computing/tech
  • To support Teacher Learning
    • Teacher candidates explore NYC 311 open data through the BetaNYC BoardStat tool to compare and contrast service requests in communities in different parts of the city.
    • Teacher candidates compare different community health variables against daily school attendance and discuss the difference between correlation and causality
    • Tell a story about a community or school by analyzing and visualizing data from a community or education data set
  • To integrate into Teacher Pedagogy
    • In their lesson/unit planning, teacher candidates demonstrate they have used student, community, or school performance data.
    • In their planning for differentiation, teacher candidates utilize not only student performance data, but also community data.
Through computing/tech
  • To support Teacher Learning
    • Data Science: Teacher candidates articulate a question about a school’s student population and find, analyze and interpret data to learn more about the question.
    • Web Design: Teacher candidate makes a informational website for parents using data about a specific school from NYC DOE school performance dashboard
    • Locating and cleaning up data sets that teacher candidates can play around with on a platform, tell stories with
  • To integrate into Teacher Pedagogy
    • Teacher candidates bring relevant community issues and successes into their instruction, equip their learners with data, and provide their learners with an opportunity to take action.
    • Teacher candidates develop a short presentation with graphs generated in CODAP about their learners for the next grade teacher that provides a holistic view of their learners
    • Support learners to design their own surveys, collect and analyze the data
Against computing/tech
  • To support Teacher Learning
    • Teacher candidates discuss the limitations of data, specifically what details of a community or student cannot be captured by data.
    • Talk back to data – how it’s used in a system, the assumptions made in its collection and visualization
    • Understand the uses and limitations of big data sets / how they are collected, what’s missing
  • To integrate into Teacher Pedagogy
    • Teacher candidates ask learners to reflect on the data variables describing them as learners and discuss aspects of themselves not represented in the data.

Summer 2022 Professional Development Workshops Related to Data Practices

  • Wednesday, July 13, 2-4pm, Privacy and Pedagogy: Resisting Data Collection and Algorithmic Biases, Hosted by Junior Tidal
  • Wednesday, July 20, 10am-12pm, Artificial Intelligence and Student Data Literacy, Hosted by Julia Stoyanovich
  • Wednesday, July 20, 2pm-4pm, Split Breakout Workshops, Hosted by Michelle Wilkerson, David Stokes, and Cherise McBride & Kathryn Lanouette

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