A new publication from the Higher Education Academy this week looks at the
needs of students for an understanding of mathematics and statistics when undertaking undergraduate studies in various disciplines.
The discipline areas studied were: Business and Management, Chemistry, Computing, Economics, Geography, Sociology and Psychology. The suggestion in the report is that many of the discipline specific recommendations are transferable to other disciplines. Notably physics and engineering are not included, as we expect students in these areas to be highly numerate.
One of the key findings is that:
“Many students arrive at university with unrealistic expectations of the
mathematical and statistical demands of their subjects. Lack of confidence and
anxiety about Mathematics/Statistics are problems for many students.”
This is worrying if students are not aware of the importance of number in their studies. As a nation we are always happy to belittle the more numerate as “geeks” whereas an ability to write in (supposedly) perfect English is seen as a strength. This is dangerous thinking – being able to make proper inferences from numbers and data is a critical skill in so many roles.
The key recommendations are:
1. There should be clear signalling to the pre-university sector about the nature and extent of mathematical and statistical knowledge and skills needed in undergraduate degree programmes.
2. As part of this signalling university tutors should consider recommending the benefits of continuing with mathematical/statistical study beyond the age of 16.
They should be aware of the full range of post-16 Mathematics qualifications, in particular the new “Core Maths” qualification.
3. Guidance documentation should be commissioned to provide university staff with a description of the range of knowledge and skills that students with
GCSE Mathematics at different grades can be expected to demonstrate when they start their undergraduate studies.
4. Key stakeholders within the disciplines should actively engage with current and future developments of discipline A-levels as well as those in post-16 Mathematics qualifications, (e.g. “Core Maths”).
5. University staff should consider the benefits of diagnostic testing of students’ mathematical and statistical knowledge and skills at the start of degree
programmes, and of using the results to inform feedback and other follow-up
actions.
6. Teaching staff should be made aware of the additional support in Mathematics and Statistics that is available to students. Students should be actively encouraged to make use of these resources and opportunities.
It’s important that we get to understand better how students transition into HE. For instance, according to the report only 13% of entrants to Psychology for example had an A-level in maths prior to entry in 2013. This will have an inevitable effect on those students’ ability to engage with any statistical tools or numerate analysis.
As well as considering the transition into HE, we should also consider how students with limited maths ability might struggle throughout their awards across a range of subjects and modules – one of the reasons that might explain low numbers of students gaining good degrees in some subjects may be their inability to engage fully with numerate modules or topics.
(from http://www.math.hope.edu/newsletter/2006-07/05-09.html)
The lack of numeracy does go further than students engaged in undergraduate study. This is a bit of a hobby horse of mine, but in years gone by, we created lists and mappings of skills that we expected to be attained by students. Communication was always included, maths never was. In our latest iteration of graduate attributes, again we recognise professionalism, team working and global citizenship, but still don’t give prominence to mathematical ability.
Long term this is concerning both for the individual and for employers – we (and other universities) might produce graduates who enter the workplace with only the flimsiest ideas of how to use number, and sometimes a too-trusting reliance on what Excel and other spreadsheets can produce.
For example, when I read a report that shows a log scale graph which the authors then claim demonstrates a linear relationship, I worry about the kind of decision-making that will be made when the base data is presented in such a skewed way.
Maybe we could look to ensure that one ways in which we differentiate Staffordshire graduates is that in future they are more numerate and data literate.