For introductory statistics education, several types of software are relevant and in use: custom designed educational programs for a specific educational goal, statistical systems for data analysis (in full professional version, in student version or as a specifically designed tool for students), statistical programming environments, spreadsheets and general purpose programming languages. We can perceive a double dilemma on a practical and on a theoretical level, which is the worse the lower the educational level we have in mind. On the one hand, we have professional statistical systems that are very complex and call for high cognitive entry costs, although they flexibly assist experts. On the other hand, custom designed educational software is of necessity constrained to enable students to concentrate on essential aspects of a learning situation and to make likely certain intended cognitive processes. Nevertheless, as these microworlds, as we will call them here, for short, are often not adaptable to teachers' needs they are often criticized as being too constrained. Their support for flexible data analysis is limited, and to satisfy the variety of demands one would need a collection of them. However, coping with uncoordinated interfaces, notations and ideas in one course would overtax the average teacher and student. This practical dilemma is reflected on a theoretical level. It is not yet clear enough what kind of software is required and helpful for statistics education. We need a critical evaluation and analysis of the design and use of existing educational and professional programs. The identification of key elements of software that are likely to survive the next quantum leap of technological development and that are fundamental for introductory statistics is an important research topic. Results should guide new "home grown" developments of educational programs or, facing the difficulty of such developments, should influence the adaptation and elaboration of existing statistical systems toward systems that are also more adequate for purposes or, facing the difficulty of such developments, should influence the adaptation and elaboration of existing statistical systems toward systems that are also more adequate for purposes of introducing and learning statistics. We will give some ideas and directions that are partly based on results of two projects.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education