Classroom data science: data and dataing for making sense of multivariate plant growth

Loading...
Thumbnail Image
File version

Version of Record (VoR)

Author(s)
Fry, Kym
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2025
Size
File type(s)
Location
Abstract

What is considered data and how it is communicated publicly is rapidly changing. Participation in society involves multiple opportunities to engage with complex statistical concepts in media, in personal health, and regarding climate change. Children also are engaging with evolving data types in social media interactions and in educational contexts where they are the data. Yet little time is spent in classrooms supporting children to explore and make their own data decisions about various data types. This study investigates the role that fourth graders’ use of graphing conventions played in supporting their conceptual development of: non-traditional data and dataing during one lesson. The author hopes to contribute a new perspective in conceptualising dataing as enculturating statistical practices around data that involves multiple features, to communicate key information, involving tools for engaging with statistics to communicate a story. In this case study, a video clip presented an entrance point in exploring multivariate data (multiple variables). Video observation and student graphs revealed insights into children’s data and dataing experiences and processes involved with multivariate data, supported by a classroom culture that valued statistical processes as a creative and meaningful, story-telling endeavour. When students were tasked with representing plant growth using a graph, displays included a wide range of variables such as patterns of leaf growth and qualitative stages of plant growth. Engaging with multivariate data in exploratory ways supported students in this study to play with graphing conventions, establishing local mathematical practices involving non-traditional data.

Journal Title

ZDM – Mathematics Education

Conference Title
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement

© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Item Access Status
Note

This publication has been entered in Griffith Research Online as an advance online version.

Access the data
Related item(s)
Subject
Persistent link to this record
Citation

Fry, K, Classroom data science: data and dataing for making sense of multivariate plant growth, ZDM – Mathematics Education, 2025

Collections