video walkthrough will show how to analyze an exam's item analysis data. So let's just search for an exam that has recently finished. So this exam, almost 2,000 people have done the exam. So we're just going to click on the name of the exam to go into the high-level statistics. So this is a very high-level statistics of how the exam has performed. As you can see, here's how many people started, how many people finished, and what the average grade is with a bell curve for the score. This is a very high level, and typically, 80% of the time, this is what you probably want to look at, just get a high-level sense of how your exam has performed. So here is difficult questions, easiest questions. And here, if you click on topics, will show you which topic that your learner is done worst in. If you uncheck this box, you will actually show all the topics, and the green meaning the highest 25%, red meaning the lowest 25%. So as you can see here, this is a very good tool to look at the data from your exam and decide which area your population is strong or weak in, so you can plan the next round of education product around. So you can see that for ELBO, people tend to score weak in, so we probably want to introduce more education content around ELBO, whereas the other topics are definitely people well-versed in, because score-wise, they're definitely doing pretty good. Alternatively, it could be that the questions around ELBO tends to be harder, so maybe next round of exam, you want to have easier questions around ELBO. Next thing you can see is that at the statistic level, here is all 200 questions and how they have performed. So this screen actually has a lot of interesting information, and you can download it definitely to Excel file if you want to download the statistics. But what is interesting is just a few highlights, right? So discrimination index, we have a documentation to describe how discrimination index is calculated, but as you can see in real time, all the user's data is analyzed and the discrimination data is calculated. If a question is used in more than one exam, then OASIS will compute discrimination index with the data for each exam individually and another discrimination index for all the product that uses the question. So let's just quickly see how the 0.38 represent. So we can mouse over here, it will show the user selection on their first attempt. We do track the last attempt, all the attempts, but first attempt is probably the most representative. So this is typically referred to as the normatic data on the user selection rate. But what is the most interesting is if you mouse over the calculator, this will show the top 27% of the student, how did they select? So you can see that the top 27% selected B, 20% of the time, and the bottom 27%, which is considered as a good student versus bad student, the bad student tend to select the correct answer 11% of the time. So that's what give this particular question a higher discrimination index, because it definitely has twice as likely that the good student select this versus the bad student, right? 20% versus 11%.

item analysis data

exam performance statistics

difficulty levels

question analysis

discrimination index

Question Rating