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Data generation in statistics – both procedural and conceptual: An inferentialist analysis
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-4185-3208
Örebro University, School of Science and Technology. University of Gothenburg, Gothenburg, Sweden.
Örebro University, School of Science and Technology. University of Cologne, Cologne, Germany.ORCID iD: 0000-0001-9530-4151
2018 (English)In: Perspectives on professional development of mathematics teachers: Proceedings of MADIF 11 / [ed] J. Häggström, Y. Liljekvist, J. Bergman Ärlebäck, M. Fahlgren, & O. Olande, Göteborg, Sweden: Svensk förening för MatematikDidaktisk Forskning - SMDF, 2018, p. 191-200Conference paper, Published paper (Refereed)
Abstract [en]

Data generation in statistics education is often conducted by the students them-selves; however, the question of what learning opportunities the data generation process offers has only been studied to a small extent. This paper investigates to what extent data generation is an observational and procedural vs. a conceptual activity. We inquire into this question based on an empirical study where eleven year old students measured the jump lengths of paper frogs. Our analysis draws on stu-dents’ discussions in group work, and it uses inferentialism as a background theory. Our results indicate that students’ discussions are conceptual to a certain extent and provide various learning opportunities for the students.

Place, publisher, year, edition, pages
Göteborg, Sweden: Svensk förening för MatematikDidaktisk Forskning - SMDF, 2018. p. 191-200
Series
Skrifter från Svensk Förening för MatematikDidaktisk Forskning, ISSN 1651-3274 ; 13
National Category
Mathematics Didactics
Identifiers
URN: urn:nbn:se:oru:diva-76045ISBN: 978-91-984024-2-1 (print)OAI: oai:DiVA.org:oru-76045DiVA, id: diva2:1348391
Conference
The eleventh research seminar of the Swedish Society for Research in Mathematics Education (MADIF11), Karlstad, Sweden, January 23–24, 2018
Available from: 2019-09-04 Created: 2019-09-04 Last updated: 2019-09-20Bibliographically approved
In thesis
1. Exploring student collaboration during Data Generation in the Statistics Classroom: An Inferentialist Perspective
Open this publication in new window or tab >>Exploring student collaboration during Data Generation in the Statistics Classroom: An Inferentialist Perspective
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This dissertation explores student collaboration during data generation in statistics. The first aim is to put the semantic theory of inferentialism to work and develop a theoretical lens for exploring student collaboration during data generation. The second is to use the previously developed inferentialist lens regarding collaboration to better understand data generation processes in the statistics classroom. Two studies were conducted in Swedish 5th and 7th grade classes. The first involved 7th-grade students collaboratively engaged in experimentation with paper helicopters and their flight durations. The second study involved 5th graders experimenting with paper frogs and their jump lengths. The analyses reveal that inferentialism is a meaningful perspective for exploring student collaboration. One salient theoretical contribution of this thesis is that the inferentialist concept of norms helps avoid the dichotomy between social and individual facets of collaboration and learning that have plagued research on collaboration. However, by using the inferentialism lens, the social and individual can be regarded in their intertwined and dynamic natures. The thesis also illustrates how the formulation of tasks, social conditions, and norms mutually condition students’ learning opportunities. It is also demonstrated that data generation processes can also involve conceptual learning opportunities. The results offer ideas concerning which classroom conditions and manners of formulating tasks may contribute to such conceptual learning opportunities

Abstract [sv]

I denna avhandling utforskas elevers samarbete under datagenerering i statistik. Avhandlingens första syfte är att operationalisera en semantisk teori, inferentialismen, och skapa en teoretisk lins för att utforska elevers samarbeten är de genererar data. Det andra syftet är att utnyttja denna inferentialistiska lins för att förstå datagenereringsprocessen i statistikklassrummet bättre. Två klassrumsstudier har genomförts. Elever i årskurs 7 fick arbeta tillsammans för att undersöka pappershelikoptrar och hur länge de kunde flyga. Elever i årskurs 5 fick samarbeta i ett experiment med pappersgrodor och undersöka hur långt de kunde hoppa. Analyserna visar att inferentialismen är ett värdefullt perspektiv för att utforska elevers samarbete. Ett framträdande teoretiska bidrag från denna avhandling är att det inferentialistiska sättet att konceptualisera normer kan användas för att undvika den dikotomi mellan individuellt och socialt perspektiv som har varit framträdande i tidigare forskning om samarbete i matematikklassrum. Genom att analysera samarbete med hjälp av inferentialistisk teori kan den dynamiska interaktionen mellan det individuella och det sociala undersökas i sin sammanflätade natur. Avhandlingen illustrerar också hur formulering av uppgifter, sociala villkor och normer tillsammans skapar förutsättningar för vilka begrepp som kommer i spel och därmed blir möjliga att lära sig. Det visas också att processen att generera data kan involvera begreppsliga lärandesituationer. Resultaten ger därför indikationer på vilka slags klassrumssituationer och vilka uppgiftsformuleringar som kan bidra till sådant konceptuellt lärande.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2019. p. 134
Series
Örebro Studies in Mathematics ; 1
Keywords
Inferentialism, Collaboration, Data generation, Statistics
National Category
Other Mathematics
Identifiers
urn:nbn:se:oru:diva-74660 (URN)978-91-7529-295-3 (ISBN)
Public defence
2019-09-27, Örebro universitet, Teknikhuset, Hörsal T, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-09-05Bibliographically approved

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Data generation in statistics – both procedural and conceptual: An inferentialist analysis(281 kB)10 downloads
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Seidouvy, AbdelHelenius, OlaSchindler, Maike

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