Open AccessDEVELOPING STATISTICAL KNOWLEDGE FOR TEACHING DURING DESIGN-BASED RESEARCH
Author(s) -
Randall E. Groth
Publication year - 2017
Publication title -
statistics education research journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.538
H-Index - 14
ISSN - 1570-1824
DOI - 10.52041/serj.v16i2.197
Subject(s) - mathematics education , unpacking , autonomy , computer science , process (computing) , statistics education , value (mathematics) , teaching method , subject (documents) , subject matter , research design , teacher education , professional development , subject matter expert , statistical analysis , psychology , pedagogy , statistics , mathematics , curriculum , artificial intelligence , linguistics , philosophy , machine learning , expert system , library science , political science , law , operating system
Statistical knowledge for teaching is not precisely equivalent to statistics subject matter knowledge. Teachers must know how to make statistics understandable to others as well as understand the subject matter themselves. This dual demand on teachers calls for the development of viable teacher education models. This paper offers one such model, which relies upon engaging teachers in design-based research. Teachers collaborate with a researcher to design, implement, and analyze instruction to pursue desired statistical learning outcomes for students. The researcher allows teachers enough autonomy to make and learn from mistakes during the process. Unpacking and addressing the mistakes has value as a means of teacher learning. The model and a specific instance of its implementation are described along with reflections on how productive mistakes during design-based research provide opportunities for fostering the development of statistical knowledge for teaching.
First published November 2017 at Statistics Education Research Journal Archives