Door Accessibility Investigation

In the Classroom
, ,
1

As part of the PIPE-LINE fellowship, yesterday my colleagues Crystal Sanchez, Matheus Bartolo Guerrero and I presented our data science lesson for elementary students called “Are the Doors in McCarthy Hall Accessible to All?”. Below is a draft summary of the lesson along with resources. We plan to grow this post into a manuscript that shares the ideas more broadly with others (via The Statistics Teacher journal). We welcome any feedback, reflections, constructive comments, or compliments.

Overview:

In this lesson, learners experience several components of the data science process in the context of accessibility of doorways in a campus building through a physical data collection process and a technological inputting data process via Google forms for initial cleaning, tidying, and questioning of the data and its attributes. Finally, an extension exists that shows how to use the free, web-based tool called the Common Online Data Analysis Platform (COCAP) to begin to represent and analyze the doorway data.

Introduction (5 min)
Physical Data Collection (15 mi)
Data Input into Google Form (5 min)
CODAP Demo (10 min)
CODAP Exploration for beginning Represent/Analyze Data (20 min)
Optional: Students share out findings (15 min)

Materials:

Pre-Lesson:

Prepare the slides, handouts (one per person), and the skeleton of the Google form.

  • Slides guide the lesson, starting with the question “what sorts of features of a doorway would you look to if you were to examine the accessibility of the doors in McCarthy Hall?” The teacher takes 5-7 ideas from the learners and types them into the slides. The goal is to have learners generate some of the attributes pre-selected by the instructors as well as get ideas for additional important attributes. This places the issue of accessibility at the center of a data science process – how can we use data science to form a question, collect/gather/clean data, analyze/represent, interpret, and tell a story about the accessibility of doors? We also wanted to engage learners in a physical and local data collection, in an environment that they experience every day, so as to start concrete before abstracting into pre-collected smaller data sets in a .csv form to larger data sets in a .csv form.
  • Handouts come with the business question, which is “are the doors of McCarthy Hall accessible to all?” It also lists six attributes we want learners to collect data on during their physical data collection so that the learners have them written down in a portable fashion. If clipboards are available, provide one per group. If floor plans for each floor of the building of inquiry are available, also provide one per group.
  • Google Form is for inputting the data into a digital format after the physical data collection process is completed. It was intentionally designed to allow the user to input a range of items for each of the six attributes – both categorical and numerical values for each input. This allows for the messiness of data to shine through (see CODAP - messy link), providing learners an opportunity to experience cleaning data and distinguishing between categorical and numerical data values. See CODAP - clean for an example of a tidied version.)

During the Lesson:

  • 0:00 - 0:05 min - set up of lesson, including brainstorming features or attributes of doors/doorways that would help determine the level of accessibility of a random door. Shares that learners will be experiencing several parts of a data science process.
    |319px;x319px;
    Assign the following roles for when groups collect door data:
    - Note taker (holds the clipboard and records observations)
    - Time Keeper (15 minutes to collect data)
    - Navigator (holds map and guides group around assigned floor)
    - Measurer (uses measuring tape)
  • 0:05 - 0:20 min - physical data collection time. Note that we included a 5 minute buffer to allow for folks to trickle back in at different paces.
  • 0:25 - 0:30 min - digital data input into the Google form. Students will submit one form per observation. Ideally, all observations are inputted into the Google form.
  • 0:30 - 0:40 min - demo some features in CODAP including: tidying data, graphing univariate variables, graphing bivariate variables, how to name a graph, how to change color of dots, and how to add measurements to the graph (proportions, measures of center, measures of spread, etc.) |373px;x238px;
  • 0:40 - 1:00 - Learners work independently / as a group at creating a CODAP
  • 1:00 - 1:15 - Student groups take turns sharing out what they have learned about the doorways of the building.

Optional Follow-up:

Given time constraints, learners may consider making a series of graphs and inserting them into a formal letter written to the State of California (or those responsible for building and maintaining the building). This letter should communicate a story, starting from learners experience collecting data and considering additional attributes, to inputting the data digitally into a Google Form, and finally to using CODAP to analyze and interpret the data. Questions for learners to consider raising in their letter:

  1. What is the general status of doors in McCarthy Hall? What forms of sampling were used in your process – simple random sample? convenience sample? stratified random sample? cluster random sample?
  2. Are they accessible to all students? Provide some concrete examples by referring to data.
  3. What areas for improvements would the learners suggest, given their knowledge of the context of the building and their class’s gathered dataset?
  4. Are there official documents that learners can reference, such as ADA Complience [https://www.dgs.ca.gov/en/CCDA/Resources/Page-Content/California-Commission-on-Disability-Access-Resources-List-Folder/ADA-Compliance---Existing-Facitlities]?
  5. What would you do differently if given the opportunity to ramp up the current study to gather data more systematically?

Lesson Summary

We hope this lesson provides elementary data scientists the opportunity to experience several components of the data science process – namely collecting/gathering data, cleaning data, organizing data, and initial stages of data representation/analysis. This lesson also focuses on the following data science concepts:

  • Understanding an observational unit (e.g. a door) and its attributes (e.g. type of handle)
  • Types of variables: Categorical and Numerical
  • Data collection (e.g. physical and digital)
  • Data organization (e.g. cleaning, tidying data)

Note to Reader
We hope to turn this lesson for elementary data scientists into a publication so that others may be inspired / build off of this lesson. Please do let us know if you have any thoughts, suggestions, ideas for improvements. Thanks!\

Bridget, Crystal, & Matheus

1 Like
Teaching and Learning Data Science in the K-12 Classroom