Analysis of passed/not passed students by enrolled year I am analysing some data from my University about successful students through years. The dataset looks like as follows
Student     Passed        Enrolled_Year
    A             0                2016
    B             1                2008
    C             1                2008
    D             0                2012
    E             1                2007
    F             1                2019
    G             1                2006
    H             1                2006
    I             0                2012
    L             1                2019
    M             0                2018
    N             1                2008

I already plotted the frequency of passed/not passed through years, to see when students, who passed exams.
I would like to know if it makes sense to look at a possible correlation between Passed and Enrolled_Year, when the number of Passed students is small compared to those who not passed.
I would like to get some insights from my dataset:
https://drive.google.com/file/d/1tUgHoFdzq2vSLHaPzAiNQC5Xkrk2SmKX/view?usp=sharing and one of this was looking at the possible correlation.
I would appreciated it if you could let me know if it makes sense a such analysis with big difference among frequency.
To start with, you could use a simple thresholding.
If you have the dataset $S$ where an element has the form $(x,y,c) \in S$, $x$ denotes the year,  $y$ is a binary value (exam passed or not), and $c$ is the student id.
you can obtain a classifier by using
$\{(x,y,c) \in S \mid x \leq \theta\}$ and $\{(x,y,c) \in S \mid x &gt; \theta\}$.
Now you can check all plausible $\theta$ values (..,2008,2009,..,2020) to see how good your data can be seperated. For example you could use Matthews correlation coefficient to evalute which $\theta$ is best.
But If you want to see if there is some linear correlation, yes you could compute the correlation coefficient, but I would assume that there is no linear correlation. Essentially you are computing the Point-biserial correlation coefficient.
Also with the thresholding method, in case you see that all $\theta$ perform more or less the same, and badly, you can see that there is no linear correlation.
You should have a look at histograms to see how your data is distributed.