It's a day before the endsemester exams and I can't help thinking about how the ERP team evaluates my feedback about professors. 10,000 + students. One obvious way which I can think of is- It gets a overall rating of each teacher. If the overall rating is good enough, I don't think they worry about the suggestion. If there is a poor rating, they would manually take some random suggestions (if they take at all).
Improper and inefficient I would say (if at all this is the process).
I am thinking of using Python's Natural Language Toolkit for this purpose. I will look for words like "good" "bad" "should" and get their context like "good at *" "bad at *" "should do *". Similar suggestions (a group of students giving similar suggestions) would be forwarded to the teacher to improve the classroom experience. Further updates after the endsem.
Improper and inefficient I would say (if at all this is the process).
I am thinking of using Python's Natural Language Toolkit for this purpose. I will look for words like "good" "bad" "should" and get their context like "good at *" "bad at *" "should do *". Similar suggestions (a group of students giving similar suggestions) would be forwarded to the teacher to improve the classroom experience. Further updates after the endsem.
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