Students' informal statistical inferences through data modeling with a large multivariate dataset

Date

2023-01-01

Author

Kazak, Sibel
Fujita, Taro
Turmo, Manoli Pifarre

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In today's age of information, the use of data is very powerful in making informed decisions. Data analytics is a field that is interested in identifying and interpreting trends and patterns within big data to make data-driven decisions. We focus on informal statistical inference and data modeling as a means of developing students' data analytics skills in school. In this study, we examine how students apply the data modeling process to draw informal inferences when exploring trends, patterns and relationships in a real dataset using technological tools, such as CODAP and Excel. We analyzed 17-18-year-old students' written reports on their explorations of data supplied by third parties. Students used a variety of statistical measures and visualizations to account for variability in analyzing data. They tended to make statements with certainty in their inferences and predictions beyond the data. When the pattern in the data was uncertain, they were inclined to use contextual knowledge to remain certain in their claims.

Subject Keywords

Data analytics, data modeling, informal statistical inference, upper secondary, CONTEXT

URI

https://hdl.handle.net/11511/102704

Journal

MATHEMATICAL THINKING AND LEARNING

DOI

https://doi.org/10.1080/10986065.2021.1922857

Collections

Department of Mathematics and Science Education, Article

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Citation Formats

S. Kazak, T. Fujita, and M. P. Turmo, “Students’ informal statistical inferences through data modeling with a large multivariate dataset,” MATHEMATICAL THINKING AND LEARNING, vol. 25, no. 1, pp. 23–43, 2023, Accessed: 00, 2023. [Online]. Available: https://hdl.handle.net/11511/102704.