Visualisation

Sometimes, school marks are moderated. That is, the actual marks are adjusted to better reflect students’ performances. For example, if an exam is very easy compared to another, you may want to scale down the marks on the easy exam to make it comparable.

I was testing out the impact of moderation. In this video, I’ll try and walk through the impact, visually, of using a simple scaling formula.

BTW, this set of videos is intended for a very specific audience. You are not expected to understand this.

Rough transcript

First, let me show you how to generate marks randomly. Let’s say we want marks with a mean of 50 and a standard deviation of 20. That means that two-thirds of the marks will be between 50 plus/minus 20. I use the NORMINV formula in Excel to generate the numbers. The formula =NORMINV(RAND(), Mean, SD) will generate a random mark that fits this distribution. Let’s say we create 225 students’ marks in this way.

Now, I’ll plot it as a scatterplot. We want the X-axis to range from 0 to 225. We want the Y-axis to range from 0 to 100. We can remove the title, axes and the gridlines. Now, we can shrink the graph and position it in a single column. It’s a good idea to change the marker style to something smaller as well. Now, that’s a quick visual representation of students’ marks in one exam.

Let’s say our exam has a mean of 70 and a standard deviation of 10. The students have done fairly well here. If I want to compare the scores in this exam with another exam with a mean of 50 and standard deviation of 20, it’s possible to scale that in a very simple way.

We reduce the mean from the marks. We divide by the standard deviation. Then multiply by the new standard deviation. And add back the new mean.

Let me plot this. I’ll copy the original plot, position it, and change the data.

Now, you can see that the mean has gone down a bit — it’s down from 70 to 50, and the spread has gone up as well — from 10 to 20.

Let’s try and understand what this means.

If the first column has the marks in a school internal exam, and the second in a public exam, we can scale the internal scores to be in line with the public exam scores for them to be comparable.

The internal exam has a higher average, which means that it was easier, and a lower spread, which means that most of the students answered similarly. When scaling it to the public exam, students who performed well in the interal exam would continue to perform well after scaling. But students with an average performance would have their scores pulled down.

This is because the internal exam is an easy one, and in order to make it comparable, we’re stretching their marks to the same range. As a result, the good performers would continue getting a top score. But poor performers who’ve gotten a better score than they would have in a public exam lose out.

This earlier visualisation was revised based feedback from teachers. It’s split into two parts: one focused on performance by subject, and another on performance of each student.

Students’ performance by subject

This is fairly simple. Under each subject, we have a list of students, sorted by marks and grouped by grade. The primary use of this is to identify top performers and bottom performers at a glance. It also gives an indication of the grade distribution.

For example, here’s mathematics.

Grades are colour-coded intuitively, like rainbow colours. Violet is high, Red is low.

The little graphs on the left show the performance in individual exams, and can be used to identify trends. For example, from the graph to the left of Karen’s score:

… you can see that she’d have been an A1 student (the first two bars are coloured A1) but for the dip in the last exam (which is coloured A2).

Finally, there’s a histogram showing the grades within the subject.

Incidentally, while the names are fictitious, the data is not. This graph shows a bimodal distribution and may indicate cheating.

Students’ performance

This is useful when you want to take a closer look at a single student. On the left are the total scores across subjects.

Because of the colour coding, it’s easy to get a visual sense of a performance across subjects. For example, in the first row, Kristina is having some trouble with Mathematics. And on the last row, Elsie is doing quite well.

To give a better sense of the performance, the next visualisation plots the relative performance of each student.

From this, it’s easy to see that Kristina is the the bottom quarter of the class in English and Science, and isn’t doing to well in Mathematics either. Gretchen and Elsie, on the other hand, are consistently doing well. Patrick may need some help with Mathematics as well. (Incidentally, the colours have no meaning. They just make it overlaps less confusing.)

Next to that is the break-up of each subject’s score.

The first number in each subject is the total score. The colour indicates the grade. The graph next to it, as before, is the trend in marks across exams. The same scores are shown alongside as numbers inside circles. The colour of the circle is the grade for that exam.

In some ways, this visualisation is less information-dense than the earlier visualisation. But this is intentional. Redundancy can help with speed of interpretation, and a reduced information density is also less intimidating to first-time readers.