Sodarace: Exploring Evolution with Computational Thinking

1. Sodarace:Exploring Evolution with Computational Thinking Paul Curzon Queen Mary University of London With support from, Department for Education, Google and the Mayor of London www.teachinglondoncomputing.org Twitter: @TeachingLDNComp @cs4fn

2. Aims • Give you deeper understanding of core topics • Computational Thinking • Computational Modeling • Computational thinking and biology (eg evolution) • Give you practical ways to teach computing and biology in a fun, thought provoking way • Linked activity sheets and booklets can be downloaded from our website: www.teachinglondoncomputing.org

3. Algorithmic Thinkingand Biology • Why does algorithmic thinking matter to a biologist? • We can build computational models (algorithms) to explore their theories • Help them better understand • Computational models also give a powerful way to learn through exploration • For example, we can explore the theory of evolution by creating a model of the way it works

4. Evolution works how? • Individual animals in a population are all slightly different • The differences are coded in their DNA • Some have differences that help them survive • Eg run faster so: escape predators or catch food better • They are more likely to have children • Children are created by sticking together half of each parent’s DNA (with random changes) • Children whose DNA codes those differences that helped their parents survive are also more likely to survive and pass the differences on…

5. Survival of the fittest • One of these creatures was designed • The other evolved from it • over many generations • The fitness test was speed • Notice how all it’s power is in its back legs • Like a cheetah or more extremely a kangeroo www.sodarace.net

6. Play with Evolution Download the Sodarace Kiosk from: sodarace.net

7. Genetic Algorithms • Use evolution to generate algorithmic solutions of problems • Model the space of solutions • represented by ‘digital DNA’ • Use mutation and crossover to produce new solutions • Compare them against a fitness test • Only the best solutions survive • Do this for many generations • better and better solutions emerge • Researchers are even exploring this to make computers creative • Evolving art • Evolving music …

8. Computational Thinking • Algorithmic thinking • Turn theories into algorithms that simulate the real world things we are trying to understand • Biology gives us new ways to create algorithmic solutions • Abstraction • Didn’t model every detail of the real world just the laws of interest • Evaluation • We use the algorithms to evaluate our understanding of the real world

9. More support On our website to support this session: • Activity sheets • Story sheets • Slides Details of more worskshops/courses • free unplugged sessions • subsidised courses (e.g. GCSE programming) www.teachinglondoncomputing.org Twitter: @TeachingLDNComp

10. Together we areTeaching London Computing Thank you! www.teachinglondoncomputing.org Twitter: @TeachingLDNComp