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High school teachers’ data set aesthetics

Victoria Delaney (Graduate School of Education, Stanford University, Palo Alto, California, USA)
Victor R. Lee (Graduate School of Education, Stanford University, Palo Alto, California, USA)

Information and Learning Sciences

ISSN: 2398-5348

Article publication date: 26 February 2024




With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic data science learning experiences with their students.


Interviews with 12 practicing high school mathematics and statistics teachers were conducted and video-recorded. Teachers were given two different data sets about the same context and asked to explain which one would be better suited for an authentic data science experience. Following knowledge analysis methods, the teachers’ responses were coded and iteratively reviewed to find themes that appeared across multiple teachers related to their aesthetic judgments.


Three aspects of authenticity for data sets for this task were identified. These include thinking of authentic data sets as being “messy,” as requiring more work for the student or analyst to pore through than other data sets and as involving computation.


Analysis of teachers’ aesthetics of data sets is a new direction for work on data literacy and data science education. The findings invite the field to think critically about how to help teachers develop new aesthetics and to provide data sets in curriculum materials that are suited for classroom use.



The authors are grateful for the participation of the 12 teachers who dedicated their time to their study.

Both authors contributed equally to this work, jointly share lead authorship and are listed in alphabetical order.

An earlier version of this paper was presented at the 2021 International Conference of the Learning Sciences and is available at The International Society of the Learning Sciences holds the copyright to that version and has provided written permission for reuse.


Delaney, V. and Lee, V.R. (2024), "High school teachers’ data set aesthetics", Information and Learning Sciences, Vol. ahead-of-print No. ahead-of-print.



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