The IDS (Intro to Data Science) project at UCLA is looking for a post-doc interested in conducting fundamental research in teaching and/or learning of data science at the high school level.  The IDS project can make available classrooms teaching the IDS curriculum (Introduction to Data Science) and will provide administrative support for IRB approvals for classroom investigations.  We are interested in projects that address basic questions such as: what are students learning in the IDS classroom? what hinders/helps this learning, what is the role of programming/coding in learning? how does what we know about statistics education help us study data science education? Is there a data science "framework" (learning trajectory/progression) that can be developed for HS and what role would a single class have?  what do students learn and think about data structures? what should they learn? does DS education foster equity? how can this be improved? what features do or don't? does DS learning enhance mathematical learning/practice beyond the DS curriculum? how does the IDS classroom learning speak to the ASA GAISE document? to the California Math Framework? to other states' standards?


While research is the primary activity, the post doc will also teach one class per year for UCLA students. This can be an undergraduate or graduate level course addressing research in statistics/data science education, equity issues in mathematics/statistics/data science education, or a class for future teachers.  We are open to suggestions.


Ideally the position will start in Fall 2024. It is a two-year position, and so the ending date would be Spring of 2026. Benefits for post-docs are described here:


Interested parties should email Rob Gould at