Orsoni Matteo, Giovagnoli Sara, Garofalo Sara, Magri Sara, Benvenuti Martina, Mazzoni Elvis, Benassi Mariagrazia
Department of Psychology, University of Bologna, Italy.
Heliyon. 2023 Mar 16;9(3):e14506. doi: 10.1016/j.heliyon.2023.e14506. eCollection 2023 Mar.
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.
在学术背景下评估学生的认知能力可为教师提供宝贵的见解，以识别他们的认知概况并制定个性化的教学策略。虽然许多研究已证明基于学生认知概况进行聚类有良好的效果，但目前的文献中缺乏对各种聚类方法的有效比较。在本研究中，我们旨在比较两种聚类技术基于学生认知能力（包括一般智力、注意力、视觉感知、工作记忆和语音意识）对学生进行分组的有效性。292名年龄在11至15岁之间的学生参与了该研究。将基于联合使用Kohonen自组织映射（SOM）和k均值聚类算法的两级方法与仅基于k均值聚类算法的方法进行了比较。然后通过AdaBoost和人工神经网络（ANN）监督算法对所得概况进行预测。结果表明，两级方法为该问题提供了最佳解决方案，而ANN算法在分类问题中获胜。这些结果为开发一种预测学生认知概况的有用工具奠定了基础。
