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Identifying the Content, Lesson Structure, and Data Use Within Pre-collegiate Data Science Curricula

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Abstract

As data become more available and integrated into daily life, there has been growing interest in developing data science curricula for youth in conjunction with scientific practices and classroom technologies. However, the what and how of data science in pre-collegiate education have not yet reached consensus. This paper analyzes two prominent self-identified data science curricula, Introduction to Data Science (Gould et al. 2018) and Bootstrap: Data Science (Krishnamurthi et al. 2020), in order to ascertain what is thus far being presented to schools as data science. We highlight overlapping content and practices by the curricula while noting some key differences between the curricula and with professional practice. Moreover, we examine how lessons are structured and what kinds of data sets are used as well as introduce a measure of data set proximity. We conclude with some recommended areas for further coverage or elaboration in future iterations and future curricular efforts.

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Notes

  1. Consider the scale difference of genomic vs astronomical data sciences.

  2. Indeed, since this analysis had been completed, the CourseKata Statistics online textbook has received investments to present itself as a statistics and data science curricular resource, albeit it is not, at the time of this writing, a National Science Foundation investment nor is it focused exclusively on pre-collegiate instruction. Similarly, the youcubed mathematics education organization at Stanford is preparing data science curricula with support that does not yet involve National Science Foundation funding.

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  1. Graduate School of Education, Stanford University, 485 Lasuen Mall, Stanford, CA, 94305, USA

    Victor R. Lee & Victoria Delaney

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Lee, V.R., Delaney, V. Identifying the Content, Lesson Structure, and Data Use Within Pre-collegiate Data Science Curricula. J Sci Educ Technol 31, 81–98 (2022). https://doi.org/10.1007/s10956-021-09932-1

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