Effect of aptitude heterogeneity on interactive classroom learning
Abstract
The transmission of information in a classroom composed of interacting students is previously mapped using a neural network. Here, the performance of the entire class based on the variability of the students' aptitude is addressed. We demonstrate that when a population of average or below average students is grouped homogenously, the average performance of the student after a student interaction opportunity (SIO) is better, as compared to a heterogeneous distribution. The results imply that low-achieving students learn better by interacting with students of same ability and that high-achieving students benefit more from fewer interaction both in same- and mixedability grouping. The study aims to develop a systematic procedure in sectioning classes to achieve optimal learning.
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