The Mastery Rubric for Statistics and Data Science (Tractenberg, LaLonde & Thornton, 2023) debuted at the May 2023 Symposium on Data Science and Statistics in St. Louis, MO (SDSS 2023) and discussed at the International Congress on Mathematics Education (ICME-15) in Sydney, Australia in July 2024. At SDSS 2023 a lightning talk and poster served to introduce the construct of the Mastery Rubric and outline the key features that will promote coherence and consistency in data science education and training. At ICME-15, the structure of the Mastery Rubric was emphasized and its utility for training Mathematics Educators (rather than practitioners/users of statistics and data science) was also discussed.
Two core assumptions of this project are:
- Data science is an extension of applied statistics; and
- In order to merit the word “science”, data science is statistics that is computationally intense and applied to solving problems in a scientific way, i.e., reproducibly and rigorously.
A two-phase cognitive task analysis was used to identify the 13 knowledge, skills, and abilities that constitute “statistics and data science”, including ethical practice.
Six stages are included: beginner; early (A1) and late (A2) apprentice; early (J1), middle (J2), and late (J3) journeyman.
The KSAs of the MR-SDS facilitate coherence in language as well as instruction for statistics, computing, and data science. The manuscript includes a figure exploring a set of three dimensional representations of “statistics and data science”. The figures feature “high” (journeyman), “medium” (apprentice), and “low” (beginner) levels of performance on the three components of statistics and data science: computing, statistics, and a third area of specialization. The series of figures allows individuals and programs to identify (or choose) the target description of themselves (individuals) and graduates/completers (of programs). The purpose of these figures is to allow individuals and programs to home in on exactly where in the Mastery Rubric they are; and for those considering training in statistics and data science, what their knowledge, skills, and abilities will (should) look like as the training begins and ends. Next steps for the scholarship include examining the alignment of the MR-SDS with a variety of undergraduate curriculum guidelines and exploring the utility of the MR-SDS for SDS program capstone courses and portfolio assessment. The MR-SDS is included in the Mastery Rubric book because of the importance of the domain across undergraduate curricula, as well as to underscore differences between curricula that target (or wish to target) statistical and data literacy vs. those targeting statistical and data science practice.