EU Code Week - Icebreaker MOOC
EuropeanSchoolNet
EU Code Week icebreaker MOOC, together with the EU Code Week Deep-dive MOOC in September, will provide teachers with the main skills and knowledge about coding and computational thinking. The final aim of both MOOCs is to train teachers on how to effectively integrate coding and computational thinking in the curriculum, regardless of the subject they teach.
Europe Code Week
CODE WEEK LEARNING BITS
October 5 - 20, 2019: a week to celebrate coding in Europe, encouraging citizens to learn more about technology, and connecting communities and organizations who can help you learn coding.
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Already tried coding before?

If you have already implemented coding in the classroom, what challenges have you faced and most importantly, how did you overcome those?



I've been participating in Europe Codeweek for three years, involving my students in many activities, technological or unplugged. I made them use code.org, scratch to build stories or games. In the last two years with some classes we have played codymaze, a team game in which you use telegrams and qrcodes with instructions to follow

Benefits for teachers to take part in EU Code Week

If you have already participated in EU Code Week, can you tell us what are the benefits you see for teachers to take part in this initiative?
I've been participating in Europe Codeweek for three years, involving my students in many activities, technological or unplugged. What is the benefit to me? It obliges me positively to increase my creativity, one of the skills I do not possess and on which I want to concentrate in the next few years.

Benefits for students to take part in EU Code Week

If you have already participated in EU Code Week, can you tell us what are the benefits you see for students to take part in this initiative?
In addition to developing computational thinking, coding also develops the ability of students to solve problems, making them more creative and motivated.
 
 

Steps of computational thinking


The computational thinking process consists of the following steps:


STEP 1 – Decomposition of the problem: Breaking the problem down into smaller, more manageable pieces of information or tasks. This makes the process more achievable, and allows the task to be tackled by a team working together, each student bringing their own experience and skills to it.


STEP 2 – Pattern recognition: Once we have decomposed the initial problem into different parts, it is possible that we find similarities or common trends in these problems – in other words, logical organisational patterns in the data. IIt is often better to solve whole classes of problems rather than just the one we have at the time: in maths, for instance, it’s much more useful to be able to do long multiplication than simply know the answer to 31 x 27.


STEP 3 – Abstraction: This is the process of simplifying things, that is, identifying what is important and separating it from the smaller details. This allows us to get solutions for different problems at the same time. Put differently, it means looking for general models that represent the patterns we recognised, removing details that are not relevant for our process. Abstraction allows us to manage this complexity by putting the detail inside a box.


STEP 4 – Algorithm design: Trying and implementing step-by-step solutions, failing, and trying again, until we find solutions that might work for this and similar problems. Often there is more than one way to solve a problem, but some ways can be quicker than others, so remember: the fewer steps to follow and the more accurate the instructions, the better and more efficient our algorithm will be.

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