It is well-observed that there are many factors that influence (bias) the results of internal evaluations at higher education institutions. Such as that male young professors in general obtain higher evaluation than their female counterparts. Therefore, should the evaluation systems consider this aspect and should we adjust them? For example, my results indicate that female professors are evaluated lower by -.159 pts (on a scale of 0-10), should the system consider this in the final reports? Should we somehow adjust the evaluation to have "more" comparable results? Similarly, if the results indicate higher evaluations for courses from last semesters (+.234 pts every 3 semesters), should this be considered? I see this adjustments a bit unethical as each professor should prepare and teach their courses in a way that will be reflected in the evaluation. Female and male professors should have the same starting line and not giving them an "artificial" support.
Very interesting. I never had such findings. But I agree that the starting point should be equal for all items of examination, the explanation should probably be looked for in other findings.
We should create an innovative internal/external evaluation system; may be artificial intelligence is the answer to all bias in internal evaluation system.
Is blanket (or even case-by-case) reverse bias an effective way to address original bias—conscious and unconscious—wherever it is found? Is it not better or at least more ethical to institute, declare, and vigorously ensure equal opportunity (the policy of treating people without discrimination based on sex, race, age, etc.)?
I think this is a useful hint, although the proportion of female professors is much higher than the proportion of male professors at the institute where I worked in the subject of General Pedagogy. And female professors have the image here that they take more time to deal with the students, also in the institute there is not a tense, but a balanced, relaxed atmosphere.
Hein Retter
Treat people without discrimination based on sex, race, age, etc. Does this mean that the internal systems should not be adjusted?
If we want a fair evaluation system, the system must be a combination of the human mind and artificial intelligence.
Best Regards @Martin Flegl
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