Industry has been pulling statistical science graduates away from academia, and from the pursuit of further postgraduate studies. In South Africa, the industry draw has resulted in a crisis in academic capacity building within statistical sciences, with two primary factors  identified as exacerbating the move away from academia: First, academic salaries in the statistical sciences are not comparable to what industry would pay at the same level of qualification (especially with the growth of ‘Data Science’); Second, the lack of sufficient supervisory skills and capacity, especially for the doctoral level, is evident across South African Statistics departments. Following the discussions documented by Fabris-Rotelli et al. (2022), there seems to be an urgent need to devise guidelines to support active early-career doctoral supervisors in South Africa (see https://sites.google.com/view/statsnetsa/).

Supervision is not the only topic that is drawing attention in Statistics Education in South Africa. The networks of Statistics Teachers that are forming in the country are beginning to share novel insights into undergraduate and postgraduate Statistics teaching, tapping into the energy that these early-career academics have for pushing boundaries within education. 

This session aims to provide a platform for academic Statisticians to share in the Statistics Education discoveries that have been made recently in South Africa, with presentations concerning undergraduate and postgraduate teaching, as well as research supervision; discoveries that have been made in spite of a Statistics Education capacity crisis.

Besides the indicated presentations, there is the possibility of additional talks by some of the panelists and discussants. These include: 1) Dr Thomas Farrar - Addressing Space Challenges at a South African University Using Auto-Generated Online Mathematical Statistics Assessments; 2) Prof. Lizanne Raubenheimer - The language challenges of teaching and supervising Statistics; 3) Prof Michael von Maltitz - A guiding rubric for the early-career doctoral
supervisor in Statistics. 

If it is at all possible, we would be delighted to expand our presentations across 2 sessions.