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Data modelling with first-grade students

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Abstract

This paper argues for a renewed focus on statistical reasoning in the beginning school years, with opportunities for children to engage in data modelling. Results are reported from the first year of a 3-year longitudinal study in which three classes of first-grade children (6-year-olds) and their teachers engaged in data modelling activities. The theme of Looking after our Environment, part of the children’s science curriculum, provided the task context. The goals for the two activities addressed here included engaging children in core components of data modelling, namely, selecting attributes, structuring and representing data, identifying variation in data, and making predictions from given data. Results include the various ways in which children represented and re-represented collected data, including attribute selection, and the metarepresentational competence they displayed in doing so. The “data lenses” through which the children dealt with informal inference (variation and prediction) are also reported.

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Acknowledgements

The project reported here is supported by a 3-year Australian Research Council (ARC) Discovery Grant DP0984178 (2009–2011). Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the author and do not necessarily reflect the views of the ARC. I wish to acknowledge the enthusiastic participation of the classroom teachers and their first-grade students, as well as the excellent support provided by my senior research assistant, Jo Macri. Professor Jane Watson’s advice (personal communication) on the statistical learning of young children is also gratefully acknowledged.

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  1. School of Mathematics, Science, and Technology Education, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Brisbane, Australia, 4059

    Lyn D. English

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Correspondence to Lyn D. English.

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English, L.D. Data modelling with first-grade students. Educ Stud Math 81, 15–30 (2012). https://doi.org/10.1007/s10649-011-9377-3

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