Students making sense of statistics through storytelling: a theoretical perspective based on Bruner’s narrative mode of thought

A persistent problem in teaching introductory statistics has been helping students overcome their fears and the abstract nature of what they need to learn. Students’ own contextualised stories are argued to present an opportunity for humanising the abstract, helping reduce student fears to complement traditional teaching approaches. This paper applies Bruner (Actual minds, possible worlds, Harvard University Press, 1986 ) theoretical perspectives on narrative mode of thought to understand how students’ own contextualised stories might support them in making sense of university introductory statistics. An exploratory, design research study was undertaken where 31 student participants were interviewed across a two-year period. All participants had completed an introductory statistics course where they wrote contextualised children’s stories about normal distributions and sampling distributions of the mean. Using an assumption-based, conjecture-driven, reflective analysis, participant interview data was analysed to generate preliminary research findings. Two preliminary findings are detailed in this paper. One revealed that participants initially don’t seem to naturally make connections with statistics using their own stories, while another showed that once they did so, their stories helped initiate pathways of access for making sense of their statistical learning. To test the preliminary findings, Bruner (Actual minds, possible worlds, Harvard University Press, 1986 ) theoretical perspectives on narrative mode of thought—presupposition, subjectification, and multiple perspectives—were used to develop an analytical tool. The methodology in the study provides new insights for understanding how students’ own contextualised stories might help them make sense of their learning. The implications of the study are relevant for statistics education, particularly in the areas of statistical thinking processes.