Unimelb Grade Distributions: Part 1
To the frustration of many students, the University of Melbourne does not publish average weighted average marks (WAMs) for the various degree programs offered. In order to remedy this absence and provide students with a sense of their relative standing, I created a survey giving students the opportunity to report their degree type (Arts, Science, etc.), WAM, what they think the average WAM in their degree is (in order to do a ‘wisdom of the crowd’ type thing) and whether or not they are happy with their WAM (in order to see how well a student’s WAM and the perceived average WAM their cohort effects their satisfaction with their own WAM).
385 people have taken the survey so far, which is a great start. With this in mind, I thought i’d make a short post to go through the results so far. I will come back to this more thoroughly later.
Before going into the results I want to make a few points.
Most importantly, the sample is clearly VERY biased. This could be due to those with lower WAMs opting out of the survey or maybe the r/unimelb subreddit just attracts a more studious, academically oriented subset of Unimelb students — maybe both. Regardless, I am certain the sample is not random. I am not saying this just because the averages ‘look’ too high. I entered my own results and checked where I ranked within my degree type. 14% of respondents in the same degree program as myself had WAMs higher than my own (even after removing entries that were obviously insincere). However, I was lucky enough to feature on the Dean’s List which means that my true ranking is somewhere in the top 3%. Hopefully this gives a sense of the severity of the selection bias so no one feels bad for having WAMs less than the apparent ‘averages’ of this poll.
With this in mind, I am considering expanding the survey to a wider audience of Unimelb students by requesting that one or more of the larger facebook groups targeted at students (i.e. Unimelb Love Letters) share a link to the survey. Other have since informed me that sharing the survey to such a wide audience (many of whom are not Unimelb students) could invite large amounts of insincere responses, so I’ll be thinking through this for a bit before making a decision — please let me know if you have any strategies for avoiding these types of problems.
I also didn’t do any analysis on WAMs between specific majors. This is mainly because there isn’t really enough data to say anything meaningful here. I’ll come back to this if the sample gets large enough though.
Finally, skimming through the survey responses, it became clear that there were a few obviously insincere, comedic answers (i.e. reporting a WAM of 99.9 and reporting dissatisfaction with it.) In order to stop these entries undermining the data, I came up with a heuristic method to eliminate entries; entries were eliminated if:
- the student reported they perceived the average WAM to be greater than or equal to 80.
- the student reported a WAM over or equal to 90 and that they were not happy with their WAM.
- the student reported either a WAM or perceived average below 50.
If anyone has any ideas of how to refine this criterion for removing ‘joke’ responses please let me know.
Results!
The number of respondents from each degree type doesn’t seem to reflect the true prevalence of undergraduate students in that program (I report the numbers in a table below) so these total averages are likely off. If anyone knows the approximate proportion of undergrads in each degree program, please let me know so I can re-weight appropriately.
Out of everyone single respondent, the mean WAM is 77 and the median WAM is 77.7 (the subreddit is filled with Unimelb’s best and brightest I suppose). The standard deviation of WAM is 6.82, and its maximum and minimum (rounded) values are 55 and 94. The perceived average WAM is 72.69.
Here are a few graphs that might be of interest before diving into each specific degree.
By degree:
I’ll dive into the results for each degree and some cool graphs but first you can get a sense of it all through this table:
Sadly, I don’t really have enough samples to conclude anything meaningful about Biomed, Design or (especially) Agriculture students.
Science:
Nothing particularly fascinating here. Science features the highest standard deviation of all degree types — perhaps this reflects the large variations in grade distributions between the large amount of major options available.
One thing to note here is that the color of the point represents whether or not a student is content with their WAM. A magenta dot means the student is happy with their WAM, a cyan dot means the opposite.
Commerce:
The commerce students surveyed seem to be a rather discontent bunch with only 48% of them happy with their WAMs. Perhaps the harsh reality that that dream audit role at KPMG could slip away at any moment keeps them anxiously on edge.
Commerce students also reported lower WAMs on average. Whether this means commerce is harder, attracts less talented students, or attracts students more secure and thus less inclined to hide their comparatively low WAMs on an anonymous online form — I will leave you to decide.
The WAM distribution for Commerce is also the prettiest of all of them.
Arts:
Arts students’ WAMs are distributed pretty similarly to those of science students. Perhaps this has something to do wih both degrees being fairly broad and less professionally oriented and therefore comparatively devoid of stress over certain hard and soft WAM cut-offs for likely future ambitions (whether in the form of grad jobs or post graduate education).
Biomed:
Another very unhappy group. The unhappiest in fact, with only 45% of respondents being happy with their WAM. No doubt this is due to the demanding WAM requirements of med school which the majority of Biomed student’s have their eyes on.
That said, with only 20 Biomed respondents, we will have to take all these results with a grain of salt.
For design students there were only 9 entries so I have not plotted any of the information.
For agriculture, there was only 1 entry. As a result, the scatter point looks quite funny with a solitary dot right in the middle:
Hopefully this has been at least somewhat informative (even with the clear selection bias skewing results so strongly). As mentioned, I plan to survey a much broader audience of Unimelb students (though I’m not sure exactly how) so hopefully I’ll be able to re-do this analysis with much more representative data sometime soon.
One last thing that may not be of interest to everyone:
I used the data to fit both a KNN and random forest classifier that attempts to predict whether a student is happy with their WAM based on both their WAM and their perceived average WAM. After a little hyper-parameter optimization, the KNN classifier featured an average accuracy score of 0.755 based on a 10-fold cross validation. The random forest classifier featured an average accuracy of 0.764.
Below, the decision boundaries for each classifier are shown when trained with the full set of data. They cyan area predicts points in {WAM, perceived average} space where a student would be predicted to be unhappy with their WAM. You can infer what the magenta area represents.
In the link below is the I used for all this. Please let me know if you have any tips, I’ve only been learning for a couple of months and am still very much a beginner:
Thanks for reading.
