Characterising the Didactic-Stochastic Knowledge of Future Mathematics Teachers: The Case of Chile

Felipe Ruz, Francisca M. Ubilla, Valentina Giaconi

Research output: Contribution to journalArticlepeer-review


Background: Stochastics’ teacher education has become an important research topic since mathematics teachers are usually responsible for the teaching of stochastics (statistics and probability) in schools. However, the emergence of new theoretical approaches has resulted in the identification of a problem: organizing and describing the necessary professional knowledge to teach stochastics. Objectives: The aim of this research is to characterize the didactic-stochastic knowledge needed for pre-service mathematics teachers, considering Chile as a case study. Design: Following a qualitative perspective, through a content-analysis. Setting and Participants: The Pedagogical and Disciplinary Standards for Mathematics Teaching Programs. Data collection and analysis: The national guidelines on the stochastic education of pre-service’ teachers were analysed to collect text fragments describing professional knowledge expected in pre-service teachers. Results: We obtained a set of 37 indicators organized according to the Didactic-Mathematical Knowledge Model, which includes disciplinary aspects (stochastic content), the knowledge of students and their learning (cognitive content) and interests (affective content), instruction processes (interactional and mediational content) and their link with the educational context and other knowledge areas (ecological content). Conclusions: We hope that the identified indicators become a useful tool to organize and evaluate stochastic education programs for pre- and in-service teachers. Moreover, we highlight the replicability of the method uses and the possible adaptation of results to other educational contexts.

Original languageEnglish
Pages (from-to)96-132
Number of pages37
JournalActa Scientiae
Issue number4
StatePublished - Jul 2023


  • Pre-service teacher education
  • Professional knowledge
  • Stochastics Education


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