Guest post by Dr. Michael Harvey

As part of preparing students for external examinations, I have been asking them about how they approach answering exam-style questions. It is something that we as educators sometimes take for granted that approaching the solving of these types of questions is actually a skill all in itself. From the student’s feedback, it was clear they were lacking a clear strategy in tackling these types of questions.

One approach that I have been introducing the students to the use of computational thinking to develop a strategy to solve long answer style questions, especially those involving calculations. Before a problem can be tackled, the problem itself and the ways in which it could be solved need to be understood. Computational thinking allows us to do this. Computational thinking allows us to take a complex problem, understand what the problem is and develop possible solutions.

There are four key techniques to computational thinking:

**Decomposition** – breaking down a problem into small more manageable parts.

**Pattern recognition** – looking for similarities within or among problems

**Abstraction** – focusing on important information only, ignoring irrelevant detail.

**Algorithms** – developing a step-by-step solution to the problem or the rules to follow to solve the problem.

Each technique is as important as the others. They are like legs on a table – if one leg is missing, the table will probably collapse. Correctly applying all four techniques will help when solving exam-style questions.

A complex problem is one that, at first glance, we don’t know how to solve easily. Students tend to try to solve the problem in one step and become frustrated when this approach fails.

So in summary, computational thinking involves taking that complex problem and breaking it down into a series of small, more manageable problems (**decomposition**). Each of these smaller problems can then be looked at individually, considering how similar problems have been solved previously (**pattern recognition**) and focusing only on the important details while ignoring irrelevant information (**abstraction**). Next, simple steps or rules to solve each of the smaller problems can be designed (**algorithms**). Finally, these simple steps or rules are used to help solve the complex problem in the best way.

So let us look at an example of how this approach can be used to solve a Physics problem.

Step 1: Break down into simpler steps. We are looking for the power, but we have been given information on the specific heat capacity, so it involves two concepts, electrical power, and specific heat capacity. So let us break the question into two.

Step 2: Pattern recognition. Write down the formulae involved in the two concepts. **P = E/t and Q = mcΔT **

Also that the electrical energy used to heat the heater is equivalent to the heat energy released by the aluminum block so **Q = E.**

With this in mind, we can start working through the problem by focusing on step 3.

Step 3: Abstract the important points from the question by highlighting them on the paper.

The metal block has a mass of 2.7 kg. The metal of the block has a specific heat capacity of 900 J/kg/°C. In 2 min 30s, the temperature of the block increases from 21°C

### to 39

°C.

So we have a mass, a specific heat capacity, a time, and a change in temperature. Looking at our equations, what part of the problem can we solve first? The thermal heat capacity as we can solve this with Q = mcΔT as we have all the variables.

This leaves us to the last step – working through the calculations stepwise. We solve for Q as we can input the mass, specific heat capacity, and change in temperature. Once we have found Q, we know E = Q, so this can be imputed into the power equation P = E/t and the question can be solved by using the time information provided in the question (remembering to convert to seconds).

The use of computational thinking approaches is an effective way to help students work through questions so that the process is not too overwhelming and helps them develop strategies to deal with complex problems which can arise. Through supporting and scaffolding students with these types of strategies it reduces extraneous cognitive load and allows students to maintain focus and succeed at these style of questions.

Computational thinking sounds like a good way to solve not only physics problems, but all manner of life’s complex problems. Enjoyed this clear explanation even though I’m far from being a teacher of mathematics or physics.

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thanks so much for reading! Agreed, it applies to everything. Great post here from Dr. Harvey!

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