Just sat through all six of the videos. VERY HELPFUL. Graphics and examples are great and help with the learning. Humor is much appreciated too! :-) THANK YOU for making these available!
Thanks Karon that was a real pleasure! This is an excellent example of how to convey this kind of material in an engaging and easily digestible way that I also found to have a very kind and pleasant tone, like you want us to learn way more than you want to show us how smart you are 🤙🏼 low-ego teaching! Thanks again 🙋🏻♂️
Thank you so much for this playlist. On CAT, when someone gets a wrong answer, is the next question is "1 degree" lower in difficulty, or it "jumps" a few degrees down in order to save time/questions to determine the test-taker score?
This series have been really helpful. You said in one of the videos you were planning on expand the content, I'd be very happy if you do :) Would it be also possible to make videos about the mathematics and equations involved? I think in your style of explaining it would be so much easier to understand it.
Very helpful. Thank you. One thing that causes confusion for me is the statement, oft repeated, that IRT is not sample dependent. Surely it must be dependent upon the sample used to define the initial parameters for both the levels of theta and the item parameters? I think there's something I'm missing here that I can't get my head around.
thanks Karon for the efforts. really informative. would like to ask you though if you can elaborate on how would the results of a test administered based on the IRT model be if the test was administered to a group of students on different days and the level of the difficulty varied clearly enough (based on the post test response from a number of students) on different days. can we expect a biasing in the results due to the day the students chose to appear for the exam?
Fran Schrag Very helpful module; the introduction of the math formula could have been enhanced, I think, if before telling us what each symbol meant, you'd spent a little more time explaining what the point of the formula was, maybe by drawing parallels with other formulas we might be familiar with. I also think at the end, you might have a summary of the advantages of IRT over the earlier test theory.
Thank you very much for the wonderful videos introducing IRT. It would be nicer if there are real examples showing how IRT works with relevant data and analytical tools.
thanks for video .it is nicely explained .but i still have one doubt . what will happen if a person appears in a multiple choice questions based exam with good knowledge, marks some easy questions wrong and difficult one right .then what will be the interpretation .please reply.
In this case, the standard error of measurement would be large and the testing would be extended (assuming CAT was using a standard error stopping rule). Assuming the mistakes were in the beginning, the CAT algorithm should adjust with a pretty good estimate, though really poor guesses in the beginning can bias the final estimate somewhat.
