The International Collaboration for Research on Statistical Reasoning, Thinking and Literacy (SRTL)

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SRTL-13 (2023)

SRTL-13 (2023)

SRTL-13 will take place in Australia from 25 June – 1 July 2023. The Theme of the Forum will be “Reconceptualising data and data-ing”.

Preliminary Announcement SRTL-13

News

SRTL-13 SRTL-13 will take place in Australia from 25 June - 1 July 2023. The Theme of the Forum will be "Reconceptualising data and data-ing".

SRTL-12 SRTL-12 took place online from 21 to 26 January 2022. The Theme of SRTL-12 was "Re-thinking learners´ reasoning with non traditional data".

A Special Issue in MTL, July 2020 Studies presented in SRTL-11 will be published in a special Issue of the journal Mathematical Thinking and Learning. The theme of this issue is: New ways of interacting with data, context and chance in statistical modeling processes. The Guest Editors are: Jennifer Noll, TERC (USA); Susanne Schnell, Goethe University Frankfurt (Germany); Rob Gould, UCLA (USA); and Katie Makar, The University of Queensland (Australia). Expected publication date is July 2020.

SRTL-11, July 14-20 2019 The eleventh gathering of the International Collaboration for Research on Statistical Reasoning, Thinking and Literacy (SRTL-11) concluded on Saturday July 20, 2019 in UCLA (Los Angeles, USA). The theme was: New ways of interacting with data, context and chance in statistical modelling processes. This year marked 20 years of the founding of SRTL in 1999. The hosts were Rob Gould and Linda Zanontian from the Department of Statistics, UCLA. 25 participants from 8 countries spend the week studying the nature and development of students’ statistical reasoning with data, context and chance in statistical modelling processes and the challenges posed to educators and researchers at all levels in supporting students to achieve these goals. We were honored by a visit of Joan Garfield, co-founder and past co-chair of the research forums. Frauke Kreuter (Germany/USA) was our Statistician-in-Residence who talked about Understanding data generation processes.

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