If you compare the end_time for one row with the start_time for the row below it, you can see often they overlap. I was initially confused by the end_time variable/attribute. Which is how EDA works right? As you look at the data and start to work with it, more questions for the data happen. Noticing these features of my text planted the seed of an idea for me to explore my “teaching vocabulary” later in the exploration. You may have noticed me talking about “lockdown” and the use of positive words like “happy”, “amazing”, and “fantastic”. The first eight lines of the caption file from my first lecture Below are the first eight rows from the caption file for my first lecture of the semester. The captions generated provide the start time and end time as well as the words spoken, remembering that the captions are automatically generated using YouTube’s algorithm, so are not 100% accurate. However, I don’t actually talk for all of this time since I try to make the lectures as interactive as possible :-). In total, for my STATS 100 (Concepts In Statistics) course this ended up being 33 lectures, or around 28 hours of “lecture talk”. I was able to obtain automatically generated captions for each of my lectures via the YouTube data API and a R package called. Since recordings are made for my lectures, I had this idea to explore the number of words I use when teaching, by analysing my lecture recordings. ![]() ![]() ![]() We were warned to practice our talks to make sure we keep to our time limit, which made me wonder how many words I could actually fit into five-minute talk. Back in June, I gave a five minute talk as part of the opening session of USCOTS – the U.S.
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