Report Bee has been analyzing the Tamil Nadu 10th and 12th Standard Board Exam results for the last 3 years. We found many interesting insights in the Board Exam results, which were used by the Tamil Nadu government to formulate new educational policies. But we felt that getting such similar insights from the learning data of primary school students would be more useful to take immediate remedial measures.

The Tamil Nadu Education Department partnered with Sarva Shiksha Abhiyan (SSA) to conduct a Baseline assessment study in the Primary (classes 1 to 5) and Upper Primary (classes 6 to 8) segments in Tamil Nadu. Naturally, the Report Bee team was excited when we were invited by the Tamil Nadu Government to analyze the performance data collected through this Baseline study.

This Baseline study was conducted in the form of a surprise test administered to a large number of school students all over the state. Students from different communities were selected for the test, to ensure unbiased outcomes. The test scores were recorded and analyzed to understand the condition of Primary and Upper Primary education in the state.

This test was done for English, Mathematics and Tamil. English and Tamil, being language subjects, were tested for Reading, Writing and Listening & Retention skills.

In typical Report Bee style, our analysis was presented in the form of rich visualizations and infographics, separately for Primary and Upper Primary students.

Some highlights from the study:

• Female students perform better than their male counterparts, as ever, in the schooling system.
• The Reading and Listening & Retention abilities of the students don’t always match, which should ring some alarm bells for educators.
• Social communities and the regional clusters have a strong impact on the student’s performance.

 About Tamil Nadu Educational District

Tamil Nadu is divided into 64 educational districts and each of the districts is further divided into multiple blocks. Each block has multiple schools under different classifications  - Government, private aided, private unaided and Adi Dravidar Welfare (ADW) schools.

 About SSA

The Government of India initiated a program called Sarva Shiksha Abhiyan (SSA) to achieve universalization of elementary education in children of 6-14 years of age. SSA is implemented in partnership with the State Governments to cover the entire country and address the needs of 192 million children in 1.1 million habitations of the nation.

At the end of each academic year, an important question that subject teachers face is, “Did any of the students improve or drop in performance over the year?” 

There are multiple tests conducted in a year and graded individually. Calculating average scores for a subject will give over-all progress of the subject in the year. However it will miss out on the actual pattern of “movement of students” from beginning of the year to end of the year. 

At Report Bee we are experimenting on new ways of presenting data that will enable teachers to identify interesting patterns. 

We tried to visualize students scores in a Mathematics across all the exams in a year. Here are the results.  

School 1: A good school that is known for good academics. 

Y axis is percentage score in a given exam.

X axis set of exams.

Students whose score changes +/ – 10% are drawn using thin green lines, scores that have 10%-20% change are drawn using red lines and any score that has changed by 30% is show using thick white line.

With that basic information, what do you observe in the above visualization of students progress in a year? Are you able to find out the set of students who have dramatically improved/dropped, or, can you find if students are consistent in their scores i.e. Good students remains good and poor student remains poor throughout the year.

Another example School 2: A school that isn’t good in academics. Subject Mathematics.

Another example School 3: School that is good in academics and extra curricular. Subject Mathematics.

We did not come to the eventual design in step-1 we had to iterate a lot, here are an example of an iteration – single color.

Our first version was drawn with uniform width lines and it was messy – you get the idea right!

While making these charts, we have ignored absentees. Good visualization is an iterative work. We still have to field test these visualization and observe if it really helps the teachers. And we plan to do lot more fine tuning.

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.

Here is the visualisation we’re working on for students’ performance.

Each row is a student’s performance across subjects. Let’s walk through each element here.

The first column shows their relative performance across different subjects. Each dot is their rank in a subject. The dots are colour coded based on the subject (and you can see the colours on the image at the top: English is black, Mathematics is dark blue, etc.)

The grey boxes in the middle shows the quartiles. A dot on the left side means that the student is in the bottom quartile. Student 30 is in the bottom quartile in almost every subject. The grey boxes indicate the 2nd and 3rd quartiles. Dots on the right indicate the top quartile.

This view lets teachers quickly explain how a student is performing – either to the headmistress, or parents, or the student. There is a big difference between a consistently good performer, a consistently poor performer, and one that is very good in some subjects, very poor in others. This view lets the teachers identify which type the student falls under.

For example, student 29 is doing very well in a few subjects, OK is some, but is very bad at computer science. This is clearly an intelligent student, so perhaps a different teaching method might help with computer science. Student 30 is doing badly in almost every subject. So the problem is not subject-specific – it is more general (perhaps motivation, home atmosphere, ability, etc.) Student 31 is consistently in the middle, but above average.

The bars in the middle show a more detailed view, using the students’ marks. The zoomed view above shows the English, Mathematics and Social Science marks for the same 3 students (29, 30, 31). The grey boxes have the same meaning. Anyone to the right of those is in the top quarter. Anyone to the left is in the bottom quarter.

Some of bars have a red or a green circle at the end

The green circle indicates that the student has a top score in the subject. The red circle indicates that the student has a bottom score in the subject. This lets teachers quickly narrow down to the best and worst performers in each subject.

The bars on top of the subjects show the histogram of students’ performances. It is a useful view to get a sense of the spread of marks.

For example, English is significantly biased towards the top half than Mathematics or Science. Mathematics has main “trailing” students at the bottom, while English has fewer, and Social Science has many more.

Most of this explanation is intuitive, really. Once explained (and often, even when not explained), they are easy to remember and apply.

So far, this visualisation answers descriptive questions, like:

• Where does this student stand with respect to the class?
• Is this student a consistent performer, or does his performance vary a lot?
• Does this subject have a consistent performance, or does it vary a lot?

We’re now working on drawing insights from this data. For example:

• Is there a difference between the performance across sections?
• Do students who perform well in science also do well in mathematics?
• Can we group students into “types” or clusters based on their performances?

Will share those shortly.