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I’ve been thinking a bit about systems thinking and reductionist thinking, especially with regard to statistics teaching and mathematics teaching. I used to teach a course on systems thinking, with regard to operations research. Systems thinking is concerned with the whole. The parts of the system interact and cannot be isolated without losing the essence of the system. Modern health providers and social workers realise that a child is a part of a family, which may be a part of a larger community, all of which have to be treated if the child is to be helped. My sister, a physio, always finds out about the home background of her patient, so that any treatment or exercise regime will fit in with their life. Reductionist thinking, by contrast, reduces things to their parts, and isolates them from their context.

Mathematics teaching lends itself to reductionist thinking. You strip away the context, then break a problem down into smaller parts, solve the parts, and then put it all back together again. Students practise solving straight-forward problems over and over to make sure they can do it right. They feel that a column of little red ticks is evidence that they have learned something correctly. As a school pupil, I loved the columns of red ticks. I have written about the need for drill in some aspects of statistics teaching and learning, and can see the value of automaticity – or the ability to answer something without having to think too hard. That can be a little like learning a language – you need to be automatic on the vocabulary and basic verb structures. I used to spend my swimming training laps conjugating Latin verbs – amo, amas, amat (breathe), amamus, amatis, amant (breathe). I never did meet any ancient Romans to converse with, to see if my recitation had helped any, but five years of Latin vocab is invaluable in pub quizzes. But learning statistics has little in common with learning a language.

There is more to teaching than having students learn how to get stuff correct. Learning involves the mind, heart and hands. The best learning occurs when students actually want to know the answer. This doesn’t happen when context has been removed.

I was struck by Jo Boaler’s, “The Elephant in the Classroom”, which opened my eyes to how monumentally dull many mathematics lessons can be to so many people. These people are generally the ones who do not get satisfied by columns of red ticks, and either want to know more and ask questions, or want to be somewhere else. Holistic lessons, that involve group work, experiential learning, multiple solution methods and even multiple solutions, have been shown to improve mathematics learning and results, and have lifelong benefits to the students. The book challenged many of my ingrained feelings about how to teach and learn mathematics.

Teaching statistics is inherently suited for a holistic approach. The problem must drive the model, not the other way around. Teachers of mathematics need to think more like teachers of social sciences if they are to capture the joy of teaching and learning statistics.

At one time I was quite taken with an approach suggested for students who are struggling, which is to go step-by-step through a number of examples in parallel and doing one step, before moving on to the next step. The examples I saw are great, and use real data, and the sentences are correct. I can see how that might appeal to students who are finding the language aspects difficult, and are interested in writing an assignment that will get them a passing grade. However I now have concerns about the approach, and it has made me think again about some of the resources we provide at Statistics Learning Centre. I don’t think a reductionist approach is suitable for the study of statistics.

Context is everything in statistical analysis. Every time we produce a graph or a numerical result we should be thinking about the meaning in context. If there is a difference between the medians showing up in the graph, and reinforced by confidence intervals that do not overlap, we need to be thinking about what that means about the heart-rate in swimmers and non-swimmers, or whatever the context is. For this reason every data set needs to be real. We cannot expect students to want to find real meaning in manufactured data. And students need to spend long enough in each context in order to be able to think about the relationship between the model and the real-life situation. This is offset by the need to provide enough examples from different contexts so that students can learn what is general to all such models, and what is specific to each. It is a question of balance.

In my effort to help improve teaching of statistics, we are now developing teaching guides and suggestions to accompany our resources. I attend workshops, talk to teachers and students, read books, and think very hard about what helps all students to learn statistics in a holistic way. I do not begin to think I have the answers, but I think I have some pretty good questions. The teaching of statistics is such a new field, and so important. I hope we all keep asking questions about what we are teaching, and how and why.

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