Hi,
when it comes to clustering students to apply diffrent pedagoical approaches by using machine learning/deep learning algorithms,
most of the researches and papers i've found,focus on Intelligent Tutoring Systems, Moocs, or any other digitally based enviromnets.
what i'm trying to do, is testing difftent instructional design models, based on features that can be extracted from a traditional class room,
and not a digital environment.
So my question is this, can you refer me to any papers which include such traits/variables that are important for clustering students in a traditional environment, like age,financial status etc?
thank you dear Ajit kumar Roy
, that certainly gives me a lot of clues. i wonder if there has been any works on a categorization of these features, in a sense that which ones are observable and detectable sooner ( in a formative way).Because if i choose such features (traits) the ML algorithm can be used as a mixed approach to clusters and do formative assessment on students in traditional class rooms, and therefor in less developed areas which is ultimately my goal.
Iman, if you have a traditional classroom, the features that you may consider are. Previous scores (You may even consider schooling scores) Gender (M,F) , Urban, rural. Using too many features may confuse you during analysis and not considered good practice.
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