Below are the results of the Teaching Excellence course as taught by Kate Benson. As this website is in Dutch, let me quickly comment on these results and explain how to read them.

First of all, there is something seriously wrong with how statistics is normally done. What is known as statistics in general, really is frequentism, the idea that the frequency of something gives you the probability of the same thing. This is wrong for a number of reason: 1) frequentism wrongly thinks that probabilities are a natural property, 2) frequentism has a circular definition of probability as the frequency of equally probable events, 3) frequencies almost never go to their logical outcome in limited numbers of trails and with infinite trials you can get any probability. For all these reasons everyone, layperson and scientist alike have to reject frequentism.

Fortunately, there is a very good alternative: subjective Bayesianism. In subjective Bayesianism an event either happens or it doesn't happen. We don't know whether it will happen or not and probability is only a measure of our uncertainty. We make subjective probability estimations based on all the experience and information we have had so far. As soon as we get new experiences or information we will update this subjective estimation according to Bayes' Rule (hence Bayesianism). This way the gap between our subjective probability estimations and the outcomes in reality will shrink the more we experience and learn. Subjective Bayesianism is completely scientific and there is no, unlike in the case of frequentism, discussion about the mathematics. On the other hand, if you are a NLP Practitioner I hope you will have recognised a lot of NLP principles in the description of subjective Bayesianism. They are a match in heaven.

As the name suggests, in subjective Bayesianism we have only personal and subjective measures of probability. This is due to the fact that everyone has had different experiences and information in his or her life. Within subjective Bayesianism people have complete freedom to take any subjective measure of probability as long as they do so sincerely. Bayes' Rule will make sure that once new information comes in, these subjective measurements are updated in light of the new experience. This has the following consequences for reading these results.

The biggest consequence is that you have to read them as being new information to you as a reader to be used to update your own subjective measurement of the value of the Teaching Excellence course. So before you start interpreting the data it is good to make explicit what your personal and subjective measurement is of the probability that the Teaching Excellence course will improve a teacher's teaching skills considerably. After you have done that, go over all the data below and check to see whether now you find Teaching Excellence to be even more likely to improve someone's teachings skills or not. In order to make sure you do this sincerely, we ask you to create a bet between two sides: Teaching Excellence improves teachings skills considerably or not. Please, send us your odds and the prices you would pay for both sides. But be aware, while you are allowed to set up the bet, we are allowed to choose which side of the bet we will take. Subjective Baysianism has proven mathematically that if you do not give sincere odds it will costs you a lot of money!

My personal and subjective measurement of the probability that Teaching Excellence considerably improves a teachers teaching skills is very high. So my subjective odds are that there is at least a 87% chance that Teaching Excellence improves a teacher's skills considerably and a 13% chance that it won't. You are welcome to take me up on this bet if you want. In order to make sure I don't lose any money, I neutralize this bet by paying out only 13 euro each time Teaching Excellence improves a teacher's skills considerably and 87 euro each time it doesn't. If my odds turn out to be correct and you chose to bet on Teaching Excellence improving a teacher's skill than for the next 100 teachers being trained by Kate Benson, I will lose 87 times 13 euro for the amount of 1131 euro, but will gain 13 times 87 euro for the exact same amount. Of course if you think my odds are wrong, you can chose a bet that will always make you money if your odds are more correct. I am happy to take you up on this bet. If you think that the odds are lower than 87% please contact me and bet that Teaching Excellence does not considerably improve a teacher's skills. If you are right, you can make up to 8700 euro for the next 100 teachers Kate Benson trains (in case no teacher's skills improve considerably). But realize that if you don't want to bet me, your subjective estimation of the probability that Teaching Excellence considerably improves teacher's teaching skills is pretty much in line with mine: 87%.

Please read the following data as follows. The first column shows the primary role of the participant: Teacher, Trainer or Coach. The second column shows you how many days have passed since they did Teaching Excellence. For now this will be 1 day, but we will ask participants to rate their performance after each semester for the coming years and update this data accordingly. By the end of 2013 this will happen for the first time and then we will add a column showing their self-assesment of their performance. For now you will see 2 topics: learning & behaviour and 3 column per topic. The first of these columns shows you how the participant did his or her own self-assesment before doing the Teaching Excellence course. The second of these topical columns shows you the expectation of his or her perfomance the participant has after doing the Teaching Excellence course. With Bayes' Rule we find the Prevision. A Prevision differs from a prediction in the sense that a Prevision implies uncertainty whereas a prediction implies certainty. As all numbers are subjective the only relevance I look at is the percentage of participating having a higher Prevision than their initial self-assesment before starting with Teaching Excellence. The Prevision will be updated as soon as we get the first results after the first semester is done at the end of 2013.

### Maandag, 3 juni 2013

#### Teaching Excellence - X updates

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