Teach Computer Science

It is a self-evident truth that the role of teachers is to facilitate student learning.  For students to learn anything there needs to be a change in their long-term memory ( Kirschner et al.,  2006 ).  The problem is that no one really understands what memory is or how to define it.  But undeterred I am nonetheless going to start with a simple model of how memory works and based on what we do know about memory I will then look at how spaced practice, low stakes testing and design of our curriculum can help with long-term memory retention. Simple model of memory There is a simple model of memory that splits memory into two components: long term memory and short term memory (Wallingham).  Our short term memory directly interacts with the environment through our senses and act as a kind of filter that determines what new learning needs to be stored in long term memory.  The problem with our short term memory is its capacity is rather limited (b...

I am currently in lockdown owing to the coronavirus pandemic that is sweeping the World.  It is apt therefore to present a model of epidemiology that shows how disease spreads through a population. Models of this type, but much more complex than the one presented here, are being used to inform national policies in order to reduce the devastating impact that the coronavirus is having on our societies. The model here is based on a very simple idea.  You have a population that starts off with a single individual who is infected with a virus and sets off the chain of infection.  That person then infects a number of other people according to the transmission rate (more on this later).  Those people then infect other people and so on. Initially the rate of growth of the number of people infected is exponential, that is a doubling of people with the disease for every round of infections, which demonstrates just how difficult it is to contain a virus once is has broken ou...

Further reading This is an article I have written elsewhere: Grey, W. Modelling the Global Carbon Cycle, Issue 10, October 2019, p 72-75

The Monty Hall problem is a probabilistic puzzle that confounds lots of people including a Nobel laurate in physics apparently! The Monty Hall problem is based on a TV quiz show and is stated in this way: A player is presented with three doors and behind one of the doors is a prize. The aim of the game is for the player to select the door with the prize behind. Of course, the player does not know which of the doors the prize is behind.   The player guesses which of the three doors the prize is behind. At that point one of the doors which the prize is not behind and which has not been selected by the player is opened. So now there are two closed doors. The player is then asked whether they wish to stick with the door that they selected or switch to the other remaining closed door. Intuitively we think of the problem like this.   Originally there is a one in three chance of us selecting the correct door.   Then one of the doors is opened and then it appears the prob...

We can use millions of random numbers to calculate Pi. This is the Monte Carlo simulation approach and is so called because it refers to the randomness inherent in casinos for which Monte Carlo is famous. This is a neat little trick and is great exercise for A level students. We start off with a quarter circle with a radius of 1 unit. We then draw a square with an area of 1 by 1 unit that encompasses the quarter circle. We know that the area of a circle, a is Pi * r * r . Given that r=1 in this case, then a is the same as Pi . Millions of points from randomly selected pairs of x and y coordinates within the square are selected. Using Pythagoras where the magnitude of a point is given by sqrt(x*x + y*y) we can determine whether a point falls inside our outside the perimeter of the circle. The radius of the circle is 1 so if the point has a magnitude of less than 1 we know that it lies within the circle.  Based on the proportion of points that fall within the perimeter of the ci...

Computational thinking is a nebulous concept that we can summarise as the ability to solve problems through the application of algorithms. The Raspberry Pi foundation consider computational thinking  to include logical thinking, decomposition, abstraction, pattern recognition, algorithm design, evaluation and organising data.  For the purposes of this article I will go with this interpretation. With the aid of concrete examples,  I will unpick each of these components in turn.   I will start with logical thinking because logic is integral to the other aspects of computational thinking. We are familiar with this way of thinking because m any puzzles and games of strategy such as chess, and solving Rubik's cube  require us to think logically.  To solve these types of problems we need to be able to think clearly and have sound reasoning.  Consider the following two statements: All computer science teachers eat cake No cake eaters cycle bicycles...