COMPARING BOX PLOT DISTRIBUTIONS: A TEACHER’S REASONING

By M. Pfannkuch. 2006

103 citations recorded by the source corpus.

Drawing conclusions from the comparison of datasets using informal statistical inference is a challenging task since the nature and type of reasoning expected is not fully understood. In this paper a secondary teacher’s reasoning from the comparison of box plot distributions during the teaching of a Year 11 (15-year-old) class is analyzed. From the analysis a model incorporating ten distinguishable elements is established to describe her reasoning. The model highlights that reasoning in the sampling and referent elements is ill formed. The methods of instruction, and the difficulties and richness of verbalizing from the comparison of box plot distributions are discussed. Implications for research and educational practice are drawn.

Subfields: Education, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4e7d880aec7faa3ef35122a2c0a389a2176742eb

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In Education

In Statistics and Probability

Citations in the corpus

Lessons from Inferentialism for Statistics Education (2011, background): “Based on our experience with stimulating ISI (Bakker, 2004a) we think that any teacher, educator, or designer who tries to support students’ informal inferential reasoning (Pfannkuch, 2006) leading to informal statistical inferences is likely to face at least three related challenges that are persis”
Telling Data Stories: Essential Dialogues for Comparative Reasoning (2010, methodology): “Does this statement presuppose some fundamental experience of sampling variability and the sample size effect (sample sizes are 20 and 17) to be able to judge that the difference in centres is “not really that large” relative to the variability? Pfannkuch (2006) notes that students often misundersta”
The Role of Context in Developing Informal Statistical Inferential Reasoning: A Classroom Study (2011, background): “Pfannkuch (2006) believed that lack of appreciation of sampling variability was limiting the students’ informal inferential reasoning.”
How Students Learn Statistics Revisited: A Current Review of Research on Teaching and Learning Statistics (2007, background): “…elementary and secondary school students that focus on understanding of particular concepts (e.g. Ben-Zvi & Amir, 2005; Cobb et al., 2003b; Pfannkuch, 2006) show that carefully designed sequences of activities using appropriate technological tools can help students improve reasoning and…”