Which features are important in determining fluency of participants measured by weighted composite score? I'm starting to learn about data science. I have sample data including different features run over several people, for each person n times. these features represent the fluency of the participants in some languages. they are including the number of correct words per sentence, the average number of the correct words in total, the average of speaking duration, the average of pauses in each turn, etc. So for features such as the number of correct words, the higher the score the better, and there is a feature such as average pauses which the lower the score the better.
I want todecide at the end which features are more significant and worth keeping and which features can be ignored.
I read about the weighted composite scores, and as far as I understood the main step is figuring out the coefficients (weights). but I didn't understand how these coefficients should be calculated. some cases are based on the analysis intuition, as I saw in some examples, but it can be so subjective.
This is a tentative answer.
One can try to determine first the relative importance of each feature (eg by Factor Analysis or Principal Component Analysis, ..)
Once the more significant features have been identified (or guessed perhaps), then one can try combinations of scores with these features.

Take simply the average as a composite score.
Do a linear regression and use the coefficients as the weights of the composite score.