This presentation is part of a Ph.D. project that aims to increase knowledge about how to support the development of teacher students’ pedagogical content knowledge (PCK) for teaching statistical inference. Dealing with the existing diversity of theoretical approaches is a well-known challenge for the research community. The focus of this short presentation is to compare two reputable frameworks: Mathematical knowledge for teaching (MKT) and Refined consensus model (RCM) for pedagogical content knowledge. This comparison will highlight their contributions, merits, shortcomings, and possible connections to evaluate and guide an on going teaching and learning design in teacher education for primary school. © Given to the authors 2022.

Recently, researchers have encouraged the teaching of statistical inference to students at all levels. However, what constitutes pre-service teachers’ pedagogical content knowledge for statistical inference has not yet been given specific attention in research. This paper presents a qualitative study of pre-service teachers participating in a collaborative learning setup in a mathematics course to be prepared for teaching statistics in primary school aged 6–10 years. The study reported here is the first cycle of a design research project. This first-phase study explores how pre-service teachers’ pedagogical content knowledge for statistical inference can be developed during their mathematics course. The findings show that pre-service teachers’ learning opportunities regarding pedagogical content knowledge for statistical inference are insufficient. Based on the initial results, an initial conjecture map was constructed that guides the forthcoming design cycle.

Denna Learning study, genomförd på en mellanstadieskola i en medelstor kommun, fokuserar på kritiska aspekter som framkommer då elever lär längdskala och hur elever ges möjlighet att urskilja dessa kritiska aspekter i undervisning. Studien har avgränsats till hur skala kan introduceras och därefter vida- reutvecklas i en visuell programmeringsmiljö. Närmare bestämt betraktas dels innebörden av skrivsät- tet för längdskala, dels övergången från en- till tvådimensionell figur. Studiens huvudresultat pekar

på vikten av att noggrant introducera skala genom att koppla skrivsätten a:1 och 1:b till motsvarande bråkform a/1 och 1/b. Därigenom erbjuds elever möjligheten att erfara proportionellt tänkande och multiplikativa processer. Studien fann även att visuell programmeringsmiljö har potential att användas i undervisning för att variera och urskilja förstoring och förminskning av tvådimensionella figurer.

This theoretical and methodological paper discusses the emerging theoretical framing and methodological considerations in our efforts to develop a theoretical approach supporting instructional design for teaching and learning statistics and mathematical modelling. From an instructional design point of view aligned with the goals in governing curricula documents and real classroom constraints, we argue for the integration of the models and modelling perspective on teaching and learning mathematics with a data-modelling approach to facilitate students’ learning statistics and mathematical modelling. An application of the framework is given and future research discussed.

The study reported in the present paper is part of a larger project, which aims to explore possibilities and challenges in developing a teaching practice that supports students’ ability to model random dependent situations by a Bayesian approach. A central premise is that modeling should be based on situations that appear realistic to the students. Given this premise, the specific purpose of the present study is to identify and characterize uncertain situations that are realistic and suitable for a Bayesian treatment. The study involves reviewing some of the literature related to Bayesian applications. Based on that review we distinguish detecting (test) situations and construction composition situations as two general types of Bayesian modeling situations.