(This article was originally published at Learn and Teach Statistics and Operations Research, and syndicated at StatsBlogs.)
“Let’s start at the very beginning – a very good place to start. When you read you begin with A, B,C!” When you do statistics you begin with…probability? the mean? graphs?
Begin at the end
But really, is the beginning a very good place to start? Sometimes, we need to begin at the end. And sometimes we need to go back before the beginning. Always we need to think about where to begin, because it is seldom obvious, and copying what other teachers and textbooks have done is often a bad idea.
Take Linear Programming, the flagship technique of Operations Research. Most text books start with a simple two variable example, one that can be drawn on a Cartesian plane. They begin by defining the decision variables and the objective function. Next they formulate the constraints and explain the non-negativity conditions. Then finally they get around to solving the problem – often through a graphical approach, and applying it to the trivial real-life imaginary example they started with.
Here is a better approach, with Linear programming as the example:
First ensure all the class members have the prerequisite mathematical skills for what you propose to teach. If they are not good at drawing equations on a plane, you will need to teach them again, or use a different approach such as using Excel Solver. If students are not sure which way around > and < signs go, you will need to go over it. If English is their second language you will need to make sure you explain words like constraint, objective and optimum. This won’t hurt the native English speakers either.
Second think about your destination. When children learn to read, they generally know what the outcome is going to be. They will be able to look at words on a page and make sense of them. When you learn to drive, you know the outcome – you will be able to get safely from one place to another behind the wheel of a car. When we learn to bake cakes, we like to have pictures of the finished product so that we can see where we are headed. Yet somehow we try to teach as if it is a voyage of discovery with no vision of the end. Now discovery is good, if it pertains to how we get to or understand a process, but students need to know what they are learning. It also helps to have a purpose. Reading, driving and baking are all purposeful, with a clear outcome. The same should be true of linear programming (or confidence intervals or decision trees or fitted lines or just about anything else we are learning.)
You give the students an illustration of the completed LP model of the problem, preferably complex enough to be realistic. You show them how it can be useful, and give them a chance to explore the model. This is SO much easier now that we have Excel and Solver to look after the solving. Let students find out all about one model and then another and another, before you begin to show how to formulate. When people know what they are trying to produce, the reasoning behind the steps is more obvious.
The same approach can be applied to teaching Linear Regression analysis. First we need to make sure that students understand what a fitted line on a graph is. Get them to interpret several fitted graphs, and use them to make predictions and write statements about the nature of the relationships modelled. Then show how to make the fitted graphs once they know why they need to.
In last week’s post I talked about histograms. Students should learn to interpret histograms and other graphs before they are required to make their own. Having to read off pie charts should help immunise them against their use.
I was in a computer lab with some students from another first year statistics course, and noticed that the first thing they were taught was how to calculate the mean and standard deviation, including the finite population correction. Was this really the most interesting way to get them introduced to the joys of data analysis and interpretation? Why start with the mean, one of the most difficult concepts in statistics?
Work backwards from the end
There is an interesting technique used for teaching skills to children with special needs. When you teach a blind child to tie shoelaces, you start at the end. You do all but the last part, and let them finish it off. This gives a sense of success and purpose. Then gradually you add the steps backwards, so that they start earlier on in the process. This also means that the part of the skill that is getting the most repetition is the new part, not the part already mastered. The same is true of memorisation. Memorise the last line first, then the last two lines etc. I suspect the same approach may well apply to more abstract skills. Maybe we should teach how to read and critique a statistical analysis, then how to write one, then finally how to do the analysis.
The spiral approach is popular, in which topics are revisited each year and built on. I would like to incorporate principles of mastery learning along with that. Mastery learning is based on the premise that you must master a skill before moving on to the next one. This is difficult to implement in a classroom, with mixed level of ability, but is more easily enacted with the help of a Learning Management System.
New math had odd beginnings
I was born in the early 1960s and was in the first cohort of children to learn “new math(s)”, devised in the US as a reaction to the humiliation of seeing the Russians put Sputnik into space before them. Even in New Zealand we were not immune to the influence of the Cold War on education! I loved our bright new textbooks, which started with Set Theory – even at age 6. Every year the first page of the text book had diagrams of herds of sheep, prides of lions and other sundry collections. I loved the Venn diagrams and the intersections – even cardinal numbers, but to this day I’m not sure how that connected with mathematics, and learning to add and subtract. And to this day I ask, “What were they thinking?” It appears that set theory is the foundation of all mathematics, and thus these mathematicians decided to start there, baffling teachers and parents alike, who were alienated by these words and symbols.
I have no doubt that the intention was to improve learning, but it seems ill-advised now. I wonder how our attempts will be viewed with the benefits of 40 years of hindsight. These days constructivism is a popular, though not universal, theory and approach to learning. The idea is that we create knowledge through adding new ideas and experiences onto our current knowledge. Sometimes that involves undoing erroneous or primitive knowledge.
Sometimes a good approach is historical – to imitate in the learner (in an accelerated form) the learning process through which mankind has progressed, preferably missing out the stupid bits. (Roman numerals are fun for some children, but pretty pointless once you realise the power of zero). It is certainly worth contemplating as an alternative approach.
This post has touched on ideas regarding the sequencing of a learning/teaching approach. There are many considerations and serious thought needs to go into where we start. Sometimes we need to start at the end.
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