Re-Thinking Learners’ Reasoning with Non-Traditional Data
This forthcoming special issue of Teaching Statistics will showcase work that was presented at SRTL-12. Many ubiquitous forms of data do not clearly fit the sample-population assumptions that underpin the statistical reasoning that has been the focus of much in statistical education. For example, data collected in real time (GPS, live traffic, tweets), image-based (photographs, drawings, facial recognition), semi-structured (scraped from social media posts), repurposed (school testing data to estimate housing prices) and big data (open access internet data, civic databases) are all examples of non-traditional data.
While non-traditional forms of data have been with us for some time, the digital age has led to a pervasive culture of data in all aspects of life, including those of our students. Widespread availability and access to myriad of non-conventional, repurposed, massive or messy data sets necessitate broadening educational knowledge to better understand how learners make sense of and interrogate data as well as how they model, analyze and make predictions from these forms of data. This special issue focuses on empirical studies that investigate or nurture learners’ understanding and reasoning with non-traditional, messy and/or complex data and models. Papers will focus on practical advice and implications for good practice in teaching statistics using non-traditional data.
This special issue will appear in mid-2023.
Jennifer Noll, TERC (USA)
Sibel Kazak, Pamukkale University (Turkey)
Lucia Zapata, University of Antioquia (Colombia)
Katie Makar, University of Queensland (Australia)