What is it about?
There are several options when grouping students in classroom, basically 3: a) Let the students group by themselves (not recommended by other studies) b) Group the students randomly (fast and fair) c) The teacher chooses the groups heterogeneously, following some criteria (complex to perform) In here, we propose an algorithm based in complex networks science that automatically group the students like the third option, given any criteria chosen by the teacher and previous information from students. Our approach outperforms the randomly formed groups.
Photo by John Schnobrich on Unsplash
Why is it important?
Other attempts have been proposed for automatic group formation. Most of them use a "genetic algorithm" or other optimization processes. We differentiate from previous attempts because we use networks to form the groups, in particular an entropy measure. Networks represent the relation between elements, so it is an appropriate tool to handle all students' relation in a classroom. Some previous attempts for group formation include the network tool, but none of them, as far as we are concerned, used entropy. Moreover, previous approaches usually do not compare their performance with other approaches. Conversely, we compare our results with a "genetic algorithm" approach.
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This page is a summary of: Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation, PLoS ONE, March 2023, PLOS, DOI: 10.1371/journal.pone.0280604.
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