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Description
Let us say we have N
reference implementations for a particular problem. The student works on the problem and submits their solution. We, using PyBryt, compare the student's implementation against N
different reference implementations. This results in N
feedback reports (what annotations are (not) satisfied in each reference implementation). The question is: What feedback do we give back to the student?
- Giving all
N
feedback reports to the student can be very confusing to the student and the student would not know what feedback to follow. - Could the solution be to derive a metric by which we can specify "how close" the student is to the particular reference implementation. This way, we provide the feedback report of a reference solution the student is most likely implementing.
- Should there be a more sophisticated logic behind the scenes? For instance, if the student imported NumPy (or created an array of zeros), it is most likely they are following a particular reference.
This is the summary of some of the open questions we started brainstorming in one of the previous tech meetings to encourage the discussion. All ideas are welcome :)
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