Teaching

Failing just hard enough to learn [Learning & Teaching / Riding higher waves Pt. 2.1]

This post is another reflection on the relationship between teaching and self-directed learning. It focuses on how to find a balance with making learning too hard or not hard enough. Thus the title: How can we deliberately make ourselves and/or our students fail just hard enough to learn? Context: I just found this post in the huge number of unfinished drafts in my WordPress. It was almost done, supposedly from early fall 2021. Some of it are reflections on my own (online) teaching in the summer term of 2021. I thought this was an interesting reflection still, so I decided to fix it up a little and post it now, despite the text not being “new” and some of my thoughts on my own teaching having changed over the last year where I have been teaching more than before as a Postdoc. Because the draft of this post was already so long and got a little longer with some 2022

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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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