Which statistical tests can I conduct to analyse the trend of series data? I have a dataset that measures students' time spent working on a set of mathematics questions. My dataframe looks a little something like this:




Participant ID
Question 1
Question 2
Question 3




1107
54.2
48.9
45.0


4208
53.1
45.6
40.6




I have times for 20 questions for about 200 students. I have observed an overall decrease in time spent per question, as is shown in the figure below:

I would like to accompany this graph with a statistical measure of negative tendency.
I don't think I should use a correlation statistic as the question number is a categorical variable.
I maybe could do a OLS regression, with X being the question number and y being the time spent per question, but I am not sure how to interpret the result.
What else could I try?

Edit
Since a few people have been asking about the context in which this data was collected, you can read all about it in the study pre-registration https://osf.io/f7zgd

Caveat The OP presents an interesting experiment that produced (up to) 200x20 = 4000 measurements. It's best to analyze the data at the student level, not the 20 averages per question, using for example spline regression as the averages don't follow a simple trend and the variances don't look constant either. That being said, the actual question is how to summarize the trend in the averages and what that trend is.
As @whuber illustrates, a plot is a great way to present 20 numbers (at least in two dimensions). If the numbers follow a pattern, this pattern can be highlighted with a judicious use of color and superimposing a line or a curve to emphasize the apparent relationship between the x and y variables.
These are powerful tools because the human visual system is very strong at seeing patterns. Sometimes however the additional graphical elements may suggest a relationship that's not strongly supported by the data.
I, for example, see a negative correlation between question order and time to answer only for questions 1 to 10. For questions 11 to 20, I see a leveling-off of average time to answer and an increase in variance (except for question 18).
Qualitatively, the data is consistent with both the "constantly negative" relationship" and "first-half negative, second-half level" relationship between question and time.
To demonstrate I plot the data twice, superimposing the two relationship patterns in each plot. I also add some commentary in the title, deliberately provoking, to stress the point I want to make with the functional relationship, in front of my imaginary audience. [For completeness, I include my "data" below.]
Neither alternative shown below is convincing (though the constant trend is less unconvincing?) It's better to fit a model that doesn't make strong assumptions about the relationship between question order and time to the full dataset of obseverations without averaging first. Perhaps spline regression, as recommended in Regression Modeling Strategies, is a place to start. See for example here.

And the alternative:

My fake data. I took the average response time from the original plot by eye. I assume time is measured in seconds because the total time is about 400 units and 400 minutes of math is a lot.
math <- tribble(
  ~question, ~time, ~sd,
  1, 40, 4,
  2, 31, 3,
  3, 27, 3,
  4, 24, 2,
  5, 27, 4,
  6, 28, 4,
  7, 26, 2,
  8, 24, 3,
  9, 21, 4,
  10, 24, 5,
  11, 13, 3,
  12, 10, 2,
  13, 13, 3,
  14, 24, 7,
  15, 17, 8,
  16, 9, 1,
  17, 16, 6,
  18, 16, 0.5,
  19, 10, 5,
  20, 11, 6
)

