Quantitative Reasoning (QR)
Although not part of the CAT4 profiles as generated by the Individual report, we understand that readers may find it helpful to consider a bias between QR and the other battery SAS results. For this reason, we include here two profiles that are not strictly part of CAT4 but can be created by teachers experienced in using CAT4 results.
- Positive quantitative bias: Students’ SAS in QR are above the other batteries.
- Negative quantitative bias: Students’ SAS in QR are below the other batteries.
What is quantitative literacy?
We know that students today can access vast amounts of information on the internet. Much of this information is quantitative in nature but, more significantly, has moved from pure research applications to the wider worlds of education, healthcare, media and entertainment, transportation and banking. This is ‘big data’, and students need to be able to analyse and understand the implications of these applications to their daily lives. All of these situations require good QR skills. QR skills cover more than mathematics. They include the ability to make sense of information displayed in different formats (graphs, tables, diagrams, equations) and the ability to convert information from one format to another.
QR is also about interpretation: using evidence to draw conclusions about data, and the ability to assess the limitations of that evidence. It is the cross-curricular nature of QR together with the developing presentation of information in mixed-media format that has resulted in the concept of ‘quantitative literacy’. Whilst students’ experience with prose literacy is seen in most subjects through a wide range of reading and writing assignments, the same is not true for quantitative literacy, even though we see its application across curriculum subjects as diverse as history, biology and economics.
Hughes-Hallett is one of those advocates for the curriculum application of this interdisciplinary nature of QR beyond mathematics and into solving contextual problems in real-world situations. One of the most obvious examples is the global issue of climate change. Larger societal issues like this require the application of QR skills in real-world contexts, whether watching the news headlines on television, reading a detailed newspaper investigation or listening to a politician talking about government policies.
Steen has posited three essential components to applying QR to real-world situations:
- engagement with the real world;
- ability to apply quantitative thinking to unfamiliar contexts;
- adaptable reasoning, or the ability to make judgements even in the “absence of sufficient information or in the face of inconsistent evidence” (Steen 2004).
In a world of ‘big data’ it is rarely possible to gather all the information necessary to make a solid judgement, and so helping students to draw conclusions in these real-world situations is a key part of any classroom use of QR.