The Fees Must Fall Commission should use data analytics
By Ekow Duker –
President Zuma appointed a Commission of Inquiry into Higher Education and Training (The Fees Commission) in January this year, to make recommendations on the feasibility of free higher education and training in South Africa. The Commission is expected to submit a draft report by mid-November and a final report two months later.
While the Fees Must Fall Campaign has evolved into something much bigger than free tertiary education, with demands such as decolonisation of universities and the curriculum; insourcing outsourced workers and accelerating the tempo of transformation, it begs the question: “What will be the basis for decision making?”
Universities sit on a gold mine of data, which can assist them to make informed decisions, and many of the larger universities have faculty who are experts in the area of data and analytics. This information should be used in conjunction with submissions from stakeholders, to make informed decisions, rather than decisions based on gut feel or emotion. The question is: “Will the Commission use data analytics for decision-making?” I suspect not.
As much as free education may be a lofty ideal, in the current economic environment with close to zero growth rates and high levels of unemployment, it is clear that we have not reached Utopia, and free higher education may remain a pipe dream.
The Commission has a very difficult task. The Commission should use data and analytics to find answers to questions such as:
- Which students are likely to be delinquent payers?
- Who are the “missing middle learners,” who are not currently supported and need support?
- Which learners are likely to pass, and should only learners likely to pass be assisted in terms of the funding of fees?
- Are there specific courses or programmes, where the completion rate is lower, and should these areas of study be funded?
- What is the correlation between the points system for entry into university applicable to specific courses and the pass rate, and should the points system be changed?
- What is the correlation between poverty and high dropout rates?
- What other factors contribute towards the 85% failure rate at universities in South Africa?
Data analytics makes sense of data and often leads to insights that conventional policy frameworks might overlook. It is for this very reason that the Commission should be using data analytics. By scoring students on their need for funding, and a wide range of non-traditional attributes such as online behaviour, data analytics can reveal the students most likely to pass, those most likely to pay their fees or part of their fees, and those most likely to repay their student loans. Data analytics could go a long way towards assisting the Commission to make recommendations that are sustainable, remedial and that also result in a good return on investment.