In the proliferation of data-centred research and development both in academic and industrial applications, blind spots emerge. There is an urgent need to increase the numbers of academic statisticians to ensure long term sustainability of South African technical prowess in applied and fundamental statistics. This work package is focussed on the development of young academics in their teaching and supervisory capacity through the impact of AI for education.
Participants
PI: Inger Fabris-Rotelli (University of Pretoria, NITheCS Associate)
Justin Harvey (Stellenbosch University, NITheCS Associate)
Ansie Smit (University of Pretoria)
Sonali Das (University of Pretoria)
Daniel Maposa (University of Limpopo)
Danielle Roberts (University of Kwazulu-Natal)
Fabio Correa (University of the Free State)
Michael von Maltitz ((University of the Free State)
Bruce Bassett (WITS and UCT)
StatSNetSA members in Statistics across South Africa from every institution
Description
There is an increasing pull of a Mathematical Sciences graduate to enter industry rather than pursue further postgraduate studies. Within South Africa, there is an urgent need to address this even more as the field of Statistics is particularly affected by the 4IR pull, resulting in a crisis in academic capacity building. Two primary factors can be isolated as those preventing the correction of this: First, academic salaries in Statistical Sciences are not comparable to what industry would pay at the same level of qualification (especially in light of the growth of ‘Data Science’). Young academics may hold out for a while but are often dissuaded by the pressure of a large undergraduate teaching load, resulting in an aging academic group within South Africa. Second, the lack of sufficient supervisory skills and capacity, especially for the doctoral level, is evident across South African Statistics departments due to limited mentorship available within the community.
In 2020, an interest group of young researchers was initiated by Inger Fabris-Rotelli to start discussing the challenges of doctoral supervision for novice and early-career supervisors in the absence of senior mentors as well as possible solutions to these challenges. This group, comprising of 7 members spread across South Africa, has been meeting virtually since mid-2020 and has been identifying, discussing, and documenting a number of issues pertinent within their own institutions – both similar and unique. This group’s meetings have shown that there is an urgent need for more support between young academics in Statistical Sciences in South Africa. This Statistics Supervision Network in South Africa (StatSNetSA) expanded on the founding members (Inger Fabris-Rotelli, Ansie Smit, Sonali Das, Daniel Maposa, Danielle Roberts, Fabio Correa, Michael von Maltitz) in 2023 to include early- and mid-career academics from across South Africa. One of the purposes of this network is to provide each other with support and guidance on how to effectively supervise Statistics doctoral students. This network was first formed during a workshop held in January 2023 in Pretoria, where discussions on navigating the early career of an academic in Statistics took place. The members of this network have regular virtual meetings to further build on these discussions with the overarching intention of building a horizontal network of academic support among the academic staff in Statistics across the country. The core group forms a research group with a number of publications and presentations already, namely:
1. Fabris-Rotelli, MJ von Maltitz, A Smit, S Das, D Roberts, G Maribe, D Maposa, FM Correa, Development of an early career academic supervisor in Statistics - A discussion towards a guiding rubric, Orion, 38(1), June 2022. 10.5784/38-1-739
2. Inger Fabris-Rotelli, Ansie Smit, Danielle Roberts, Daniel Maposa, Fabio Correa, Michael von Maltitz, Sonali Das, South Africa is short of academic statisticians: why and what can be done, 10 April 2024, The Conversation. Also published here and interviewed live on TV on 10 April 2024 on Newzroom Afrika, 14:15 – 14:30 (recording).
3. Michael J. von Maltitz, Inger Fabris-Rotelli, Ansie Smit, Danielle Roberts, Sonali Das, Daniel Maposa, A Guiding Rubric for The Early-Career Doctoral Supervisor in Statistics, 6th International Academic Conference on Education, 24-26 March 2023, Oxford, UK, IAC Education.
Objectives
The founding members of StatSNetSA continue their research, currently investigating the impact of various AI tools in supervision, specifically in Statistics. The role in Statistics research is complex since the novelty comes through new methodological advances in modelling and inference, and involves extensive coding. The ability of supervisors to advise on the use of GenAI and spot the use of it is limited due to the newness of these tools. Little is understood in general, but our research aims to provide advice to novice supervisors, and through them to postgraduate students in Statistics, and to understand the role it is playing in academic Statistics. Overall, the group aims to continue such important education research in the Statistics theme as well as the Data Science theme. Such research extends easily into the other themes of NiTheCS.