Didactical Reduction, Part II

In my first post on didactical reduction, I argued that reduction of learning materials to meaningslessness can be detrimental, that teachers should trust in their students’ ability to learn and rise to a challenge. In this post I want to discuss ways of reducing complexity which actually makes sense. The gist is: reduce unneccessary detail, not difficulty. Build complexity in a carefully chosen progression. Telling the difference between unnecessary detail and challenging complexity In my post on why programming classes fail and learning ‘algorithmic thinking’, a main example was that students starting out programmig don’t need to know about data types. I will stick with this example here because I just think it illustrates my point so well. The skill to learn I discussed in the post really wasn’t the ‘vocabulary’ of your first programming language, but ‘learning programming’ means successfully communicating with a computer and in order to do that, you need to develop the skill of algorithmic thinking. This

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Why most “learn programming” classes, books and attempts fail

This seems to be a bold claim. Let me explain… There are two reasons why I think most introductory programming classes fail ant that is a) because they never actually teach prorgramming (i.e. “algorithmic thinking”, not the syntax of one concrete language / “your first language”)) and b) because they bombard students with tons of complicated subjects which are not necessary at the beginning, so nobody remembers or understands them anyway. But they confuse the students and distract them from what they really should learn like how to interact with the machine and basic flow control. Use a visual language (like Scratch for PC or Catrobat for mobile devices) and thank me later. Algorithmic thinking When we want to learn or teach how to program, we first need to define what programming is. Like in a human language, knowing the words and the grammar is not enough – knowing a language means “knowing how to communicate using that language”. For

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On Didactical Reduction (especially in the DH)

Didactical reduction means abstracting complexity to facilitate learning. It is the act of reducing and simplifying teaching material as to promote student learning. Sadly, I feel that didactical reduction doesn’t accomplish its ends most of the time. Here is why and how I think we could do better. The road to hell is paved with good intentions I have seen many classes where the content to be taught was reduced so drastically that is became simple and clear – but maybe **too simple and clear. It became meaningless.** The material became so easy to understand (or, even worse, a complex topic was made to seem like a banality), so that students stopped paying attention. “I already know this” or “I get this” are not necessarily thoughts a teacher wants to provoke. We use didactical reduction so that the complexity of a topic is hidden and we don’t scare our pupils. But fear isn’t always a bad thing. Fear means respect. And

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The Name of the Game: How to pronounce LaTeX correctly (once and for all – or not)

In this post, I want to address the question of how to pronounce “LaTeX”. And explain to you, why this question cannot be answered if you want to take the investigation seriously. It turns out to be a classical Humanities’ style thinking kind of logical dilemma.  Unlike other explanations, I dont’ want to provide an answer (because there isn’t one) but rather explain why this question is not just a matter of “knowing how it’s pronounced” and thereby, show why we need the Humanities. Description of the current situation There are multiple pronounciations currently in use which are all considered to be more or less valid. But it can sometimes come to holy wars if you refuse to adopt the accepted pronounciation at large in your social or work environment. This is why I want to prove in this article that the question of how to pronounce “LaTeX” is actually a logical dilemma we can’t solve. I thus invite everyone

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