It would appear that I am not the only one with a love of SID Learning either. Ofsted are with me all the way. Check out these quotes from the 'Moving English Forward' document published earlier this year.
"While pace is important teachers too often concentrate on the pace of their planned activities rather than the pace of learning."
"This is often counterproductive, as activities are changed so often that pupils do not complete tasks and learning is not consolidated or extended."
"A constant criticism from inspectors was that pupils rarely had extended periods to read, write or discuss issues in class."
"Pupils need time to complete something before they can valuably discuss and evaluate it."So here is the first of two posts outlining two recent three-hour lessons in which I used Slow, Interdependent, Deep Learning activities to allow students to fully explore sociological theory and empirical evidence in two different collaborative settings. Our topic at the moment is 'Demographic Trends in the United Kingdom' which is probably the least oomphy part of the whole A-Level, introduced a few years ago with very little obvious coherent link to the Family & Households topic. It is also heavily data-driven and so can end up lending itself to didactic forms of teaching, which just isn't my thing. So here's how I've tried to slow it down and make it interdependent in order to generate deep understanding of the core strands of birth rates versus life expectancy (natural change) and immigration versus emigration (migration) and the causes and effects of these social phenomena.
The result of the activity was phenomenal. Without any guidance from me, other than in the selection of the materials presented to them, the groups were each able to identify that there had been a notable expansion in the population of the United Kingdom (they didn't agree if it was the biggest in half a century). They were also able to identify, with minimal support (I had told them that I would only answer their questions about data with a 'yes' or a 'no' in order to get them to think more carefully about what they asked), that there had been a relative baby boom amongst first generation immigrant mothers.
But it was the troublesome word "fuelling" that most challenged them (and so it should) and led them to evaluate the reasons behind the choice of the word for editorial, political and social reasons. Two groups understood that there was something not quite right with the word, but it was in the presentation of the final group that they finally understood, because they had used their mathematical skills to work out that the "immigrant baby boom" only accounted for five percent of the total population growth. They then used the other information that they had to correctly hypothesise that it was more likely to be reduced mortality rates amongst the elderly that was most likely to have "fuelled" population growth. This led us on to a discussion of the impact of an ageing population on the economy and upon political structures, as well as upon the families and households of the UK: in short, my next lesson with them.
But it wasn't ever about the product, however wonderful it may be when students find their way to an excellent outcome. It was the process of students interacting with complex data in order to investigate a highly controversial hypothesis about a potentially troubling social phenomena whilst drawing in their existing theoretical knowledge and understanding that made this lesson a great example of the power of Slow Learning, Interdependent Learning and Deep Learning.