Preliminary evidence on machine learning approaches for clusterizing students' cognitive profile.

ABSTRACT: Assessing the cognitive abilities of students in academic contexts can provide valuable insights for teachers to identify their cognitive profile and create personalized teaching strategies. While numerous studies have demonstrated promising outcomes in clustering students based on their cognitive profiles, effective comparisons between various clustering methods are lacking in the current literature. In this study, we aim to compare the effectiveness of two clustering techniques to group students based on their cognitive abilities including general intelligence, attention, visual perception, working memory, and phonological awareness. 292 students, aged 11-15 years, participated in the study. A two-level approach based on the joint use of Kohonen's Self-Organizing Map (SOMs) and k-means c

SUBMITTER: Orsoni M 

PROVIDER: S-EPMC10031485 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature