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Figure of the Day: Identifying Ambiguity and Biases in Data Figures

Author(s): Raisa Hernández-Pacheco

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The adaptation focuses on presenting figures with all the information in their axes. However, such figures present a medium level of quality to encourage students to discuss conceptually in groups and come up with suggestions on how to improve them.

Licensed under CC Attribution-NonCommercial-ShareAlike 4.0 International according to these terms

Version 1.0 - published on 21 May 2020 doi:10.25334/EH5E-8F72 - cite this

Adapted from: Figure of the Day v 1.0


Specific details:

I carried out this activity during the first 3-5 minutes of each class section (twice a week). My classroom was organized in tables of ~6 students. Each table had a TV monitor where I projected the figure (Active Learning Classroom). Thus, it served as a routinized activity to make students talk to each other during those first minutes of class. After their brief discussion, I went to each table asking for a single observation, or more specific questions regarding patterns, statistics, and interpretation. I started the adaptation after implementing the original module for several weeks. I decided to add this adaptation in order to evaluate the students and see whether they have learned to identify ambiguity and biases in data figures and thus provide a discussion on how to avoid it when making their own figures.

Student Learning Outcomes:

  • Describe patterns in data using figures
  • Identify appropriate data visualization practices for different variable types


Universal Design for Learning Guidelines:

This adaptation was framed under UDL guidelines provided by CAST to facilitate the module to a diverse audience and reinforce the learning outcomes. Because I envisioned the activity as an engagement “ice-breaking” tool during the first minutes of each class section, I focused on UDL guidelines for engagement and representation:

  • Provide Multiple Means of Engagement. (1) Minimize threats and distractions - all students are participants with no risk of being wrong and there is still surprise in the routinized activity. In this activity, students are not graded on the content of their answers and thus, there is no risk of being wrong. Each answer should facilitate the conversation, rather than limit it. (2) Foster collaboration and community - discussions are in groups. Students are self-organized in collaborative tables of up to six students. Thus, it is expected that students will feel comfortable sharing ideas among them. During the first 2-3 minutes of the activity, each group is allowed to share ideas independently from other groups. In this way, all members of the group are encouraged to participate in the “private atmosphere” of the group. During the larger class discussion, only those wishing to participate as voluntary representatives of each group would do so.
  • Provide Multiple Means of Representation (1) Highlight patterns, critical features, big ideas, and relationships - multiple examples and non-examples are provided to emphasize critical features. Data visualization through figures share common features that harmonize with the statistics underlying it. Thus, it is important that students are able to connect data visualization with the statistics being used. In turn, that allows them to understand the research question motivating the study. By providing examples and non-examples, students can identify critical features in data visualization and connect it to the research question and hypothesis. (2) Maximize transfer and generalization - multiple opportunities for review and practice are provided during the semester. This is a no-risk practice and divergent thinking is encouraged.

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