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 clustering algorithm was compared with an approach based on the k-means clustering algorithm only. The resulting profiles were then predicted via AdaBoost and ANN supervised algorithms. The results showed that the two-level approach provides the best solution for this problem while the ANN algorithm was the winner in the classification problem. These results laying the foundations for developing a useful instrument for predicting the students’ cognitive profile.

Matteo Orsoni, S.G. (2023). Preliminary evidence on machine learning approaches for clusterizing students’ cognitive profile. HELIYON, 9(3), 1-11 [10.1016/j.heliyon.2023.e14506].

Preliminary evidence on machine learning approaches for clusterizing students’ cognitive profile

Matteo Orsoni
Primo
Writing – Original Draft Preparation
;
Sara Giovagnoli
Secondo
Conceptualization
;
Sara Garofalo
Methodology
;
Sara Magri
Methodology
;
Martina Benvenuti
Writing – Review & Editing
;
Elvis Mazzoni
Penultimo
Writing – Review & Editing
;
Mariagrazia Benassi
Ultimo
Supervision
2023

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 clustering algorithm was compared with an approach based on the k-means clustering algorithm only. The resulting profiles were then predicted via AdaBoost and ANN supervised algorithms. The results showed that the two-level approach provides the best solution for this problem while the ANN algorithm was the winner in the classification problem. These results laying the foundations for developing a useful instrument for predicting the students’ cognitive profile.
2023
Matteo Orsoni, S.G. (2023). Preliminary evidence on machine learning approaches for clusterizing students’ cognitive profile. HELIYON, 9(3), 1-11 [10.1016/j.heliyon.2023.e14506].
Matteo Orsoni, Sara Giovagnoli, Sara Garofalo, Sara Magri, Martina Benvenuti, Elvis Mazzoni, Mariagrazia Benassi
File in questo prodotto:
File Dimensione Formato  
preliminary evidence.pdf

accesso aperto

Descrizione: articolo pubblicato
Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
Dimensione 653.76 kB
Formato Adobe PDF
653.76 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/920851
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 3
social impact