Learning with large, complex data and visualizations: youth data wrangling in modeling family migration

Jennifer Kahn, Shiyan Jiang

Research output: Contribution to journalArticlepeer-review

Abstract

We present a micro-analysis of youth interactions with large complex, socioeconomic datasets and data visualization tools. Middle and high school youth used georeferenced data and data visualization tools to assemble models that present their family migration histories in relation to larger socioeconomic trends in a summer program. Using screen-capture and video recordings, field notes, and artifacts, we analyzed youth’s step-by-step decision-making and interaction with data interfaces in data wrangling, which we define as the practices for selecting, interpreting, and integrating datasets in order to build meaningful data displays and tell a story with the data. We identify patterns in youth’s data wrangling trajectories and propose a conceptual model for describing the stages (Find, Relate, Challenge, Build) of youth learning to construct models and tell stories about family migration. In addition, we highlight student struggles and opportunities for learning to be explored in future learning environment designs with large, complex datasets and data interfaces.

Original languageEnglish (US)
Pages (from-to)128-143
Number of pages16
JournalLearning, Media and Technology
Volume46
Issue number2
DOIs
StatePublished - 2021
Externally publishedYes

Keywords

  • Data wrangling
  • critical data literacy
  • data modeling
  • data visualization
  • family migration

ASJC Scopus subject areas

  • Education
  • Media Technology

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