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Humboldt-Universität zu Berlin - Humboldt-ProMINT-Kolleg

Humboldt-Universität zu Berlin | Humboldt-ProMINT-Kolleg | Humboldt-ProMINT-Kolleg | Kollegiat_innen | Measurement uncertainties as a means to enhance students' ability to compare data

Measurement uncertainties as a means to enhance students' ability to compare data

Measurement uncertainties as a means to enhance students' ability to compare data

Judging the quality of data is a competence that all students should have (Chinn & Malhotra, 2002). Being able to interpret this data is growing more and more important in our technological society (Sharma, 2006). Nevertheless, Kok, Priemer, Musold, & Masnick, (2019) have shown that showing more exact data (i.e. better quality) results in poorer decisions when comparing two data sets. In this study, students were introduced to an experiment in which the same quantity was measured six times, using two setups. The students were asked to compare the data of the two setups and judge whether the results were in agreement or not. They found that, with more decimal places, students’ ability to compare data sets was reduced. With more decimal places, measurement uncertainties become more explicit. Measurement uncertainties is a topic that is often neglected at schools (Priemer & Hellwig, 2018), and students often experience difficulties with this topic (Garfield & Ben‐Zvi, 2007; Ludwig, Priemer, & Lewalter, 2018; Priemer & Hellwig, 2018). The authors claim that this is the result of students’ inability to quantify the difference between two mean values, i.e. they lack the concept of an uncertainty interval.

In this study I aim to improve students’ ability to compare data sets by teaching them about measurement uncertainties. This topic is introduced through a series of interventions, based on the concepts of the model by Priemer & Hellwig, (2018).

 

References:

Chinn, C. A., & Malhotra, B. A. (2002). Epistemologically Authentic Inquiry in Schools: A Theoretical Framework for Evaluating Inquiry Tasks. Science Education, 86(2), 175–218. https://doi.org/10.1002/sce.10001

Garfield, J., & Ben‐Zvi, D. (2007). How Students Learn Statistics Revisited: A Current Review of Research on Teaching and Learning Statistics. International Statistical Review, 75(3), 372–396. https://doi.org/10.1111/j.1751-5823.2007.00029.x

Kok, K., Priemer, B., Musold, W., & Masnick, A. (2019). Students’ conclusions from measurement data: The more decimal places, the better? Physical Review Physics Education Research, 15(1), 010103. https://doi.org/10.1103/PhysRevPhysEducRes.15.010103

Ludwig, T., Priemer, B., & Lewalter, D. (2018). Decision-making in uncertainty-infused learning situations with experiments in physics classes. In Proceedings of the Tenth International Conference on Teaching Statistics (ICOTS10, July, 2018). Kyoto, Japan: Voorburg, The Netherlands: International Statistical Institute.

Priemer, B., & Hellwig, J. (2018). Learning About Measurement Uncertainties in Secondary Education: A Model of the Subject Matter. International Journal of Science and Mathematics Education, 16(1), 45–68. https://doi.org/10.1007/s10763-016-9768-0

Sharma, S. V. (2006). High School Students Interpreting Tables and Graphs: Implications for Research. International Journal of Science and Mathematics Education, 4(2), 241–268. https://doi.org/10.1007/s10763-005-9005-8