Due the stressy week with a lot of final exminations (Web applications and practical projects, algorithms, Didaktics) and my job I forgot to post my weekly review.
The last week was full of good reports on mobile learning. I mostly figured out that mobile learning units should serve contents that match the conditions of mobile learning: e.g. noise, smell, the need to change trains constantly, overcrowded trains etc. Therefore, the portions should be as small as possible and the unit should support pausing the learning process without loosing the current state.
I concluded that really “deep” learning – meaning crossing multiple layers of Bloom’s taxonomy – can’t really be achieved by small-portioned mobile learning units because they don’t allow to focus and concentrate in learning units that have a lot of contents. So, mobile learning units like finding the capitals of countries really work well – your brain only has to store the pair capital/country and the question can be repeated constantly which allows to store those small learning portions.
Some of the reports showed me a lot of good elements that each mobile learning unit should include in my opinion: such as downloading and notes functionality. Taking notes should be as easy as possible without having to switch apps. There were good examples of apps where you could even take some notes and refer to certain mark on the video timeline – great!
I also liked additional didactical methods that some learning units came up with, like the exercises (multiple-choice) that you needed to do after the videos. The learning unit designers should also deal with weak internet connection problems which you have when you’re traveling around via train.
I made a plan for my last two weeks in the semester. Unfortunately, I broke it up because I became sick on Thursday morning including fever. So I couldn’t join the IMI show time which made me really sad because we did a great project and I couldn’t be there to present it
For the next week, I will prepare for the didactics exam and finish the algorithm & optimizations homework.