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Living the DReaM: The interrelations between statistical, scientific and nature of science uncertainty articulations through citizen science.

Keren AridorMichal DvirDina TsybulskyDani Ben-Zvi
Published in: Instructional science (2023)
Responsible citizenship and sound decision-making in today's information age necessitate an appreciation of the role of uncertainty in the process of generating data-based scientific knowledge. The latter calls for coordinating between different types of uncertainties, related to three types of relevant reasoning: statistical, scientific, and nature of science uncertainties. This article examines separately the uncertainties that young students articulate as they engage in activities designed to concurrently foster all three types of reasoning, and also explores how these different types can interrelate. The context of Citizen Science is particularly suited for this goal, providing a unique pedagogical opportunity for learning scientific content by engaging learners in authentic scientific practices, including data analysis. Based on literature from the three fields of statistics, science and nature of science education, we offer an integrative framework, Deterministic Relativistic and Middle ground (DReaM), which consists of nine sub-categories of uncertainty articulations. We utilize it to analyze an instrumental case study of a pair of middle school students' (ages 13 and 14) participation in a pilot study of an interdisciplinary extended learning sequence, as part of the Radon Citizen Science Project. The results of an interpretative microgenetic analysis identified all nine DReaM uncertainty articulations sub-categories. These are illustrated in the Findings section with key scenes from the pair's participation. The discussion depicts how these sub-categories manifested in this particular case study and suggests interrelations between them in a more extended depiction of the DReaM framework. We conclude with the pedagogical implications of the extended framework.
Keyphrases
  • public health
  • data analysis
  • healthcare
  • systematic review
  • quality improvement
  • big data