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Lesson Objectives Aims Key Words

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1 Lesson Objectives Aims Key Words
To recognise the different sections of 2.2 – Problem solving and programming To recap some basic programming theory from GCSE Computer Science (all that time ago) Key Words

2 2.2 Problem Solving and Programming
What do you need to get to know?

3 2.2.2 – Computational Methods
So we’ve looked at how to think computationally and the important programming techniques. Now we need to be able to apply these to approach problems in a computer sciencey* way! (*not a word) Computer Science ftw A Computer Scientist according to Google… look how happy she is.

4 We’re used to solving problems in life all the time.
Problem Solving We’re used to solving problems in life all the time. Stressful high pressure problems such as: What is the best voucher to use when buying a Dominos (you’re a moron if you pay full price) We’re used to solving most problems without thinking, using instincts or habits. If you’ve solved a similar problem before, it makes the new one a lot easier!

5 A Problem! Liz Truss has a trade mission to China to discuss new pork markets. It’s very important and she must check-in in two hours. Before she leaves she has to take her goldfish to the goldfish-sitter… Liz is all ready to leave when she realises her fish has gone out and not left a note. Liz can’t leave her fish alone for two weeks – will she have to abandon the new pork markets?

6 A Problem Liz could just panic. She could run around the local area looking for her fish madly but the fish knows what’s coming and is probably hiding! Liz checks his favourite hair salon to no avail.. She starts checking places she wouldn’t usually expect to find him in… kitchen cupboards, the loft etc etc. Time is ticking away; Liz is stressed and frantic. The whole time Liz stressed and ran around looking, the fish was having a bath upstairs. It’s time Liz spiced up her life and sorted it out.

7 A Problem A more sensible solution! Liz could have planned ahead! If the fish had gone to the fish-sitter the day before then it would’ve been dealt with, giving her plenty of time to catch the Beijing flight. What other steps could Liz take to ensure her trip is a success?

8 Another Problem You’ve got an essay to write for Mr. Davidson on the benefits of beards in Computer Science. The deadline is approaching. What can you do? 1. Put it off, ignore it until the night before – let’s be honest; it’s not going to be any good. 2. Plan plan plan! Do some research, organise your thoughts, make notes, devise a structure before you start the essay.

9 More commonplace example of heuristics is “rule of thumb”.
A key term you need to understand is heuristic. This basically means to find a solution that is “good enough”. Heuristic makes use of past experience – there’s no guarantee that it’s the best solution though. More commonplace example of heuristics is “rule of thumb”. “a broadly accurate guide or principle, based on practice rather than theory.”

10 Heuristics Important thing with heuristics is to know when a solution is or isn’t good enough. A good example of heuristic problem solving day-to-day is crossing a busy road. You don’t spend time calculating the exact speed of cars etc… you can take in some information quickly – approx car speeds (fast or slow), where the cars are, are the drivers paying attention to you? Your brain processes all this information and matches it with previous road crosses so you can chose a good moment to cross.

11 Backtracking Backtracking is a common algorithmic approach to problem solving. At its simplest – it’s trial and error. You have a large problem… you try to solve small parts of it at a time, only moving on when the attempt was successful – eventually building a complete solution. If there’s a failure along the way you discard everything until the last point that was successful.

12 Review/Success Criteria
You should know The basic constructs of code: Sequencing Branching Iteration (do/for/while) You should be able to confidently and independently use all three of these constructs to build effective and totally not rubbish programs


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