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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 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.

SUBMITTER: Orsoni M

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

REPOSITORIES: biostudies-literature

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

Orsoni Matteo M Giovagnoli Sara S Garofalo Sara S Magri Sara S Benvenuti Martina M Mazzoni Elvis E Benassi Mariagrazia M

Heliyon 20230316 3

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 studen ...[more]

PMID: 36967938

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Project description:IntroductionSince the advent of medical education systems, managing high-stakes exams has been a top priority and challenge for all policymakers. However, considering machine learning (ML) techniques as a replacement for medical licensing examinations, particularly during crises such as the COVID-19 outbreak, could be an effective solution. This study uses ML models to develop a framework for predicting medical students' performance on high-stakes exams, such as the Comprehensive Medical Basic Sciences Examination (CMBSE).Material and methodsPrediction of students' status and score on high-stakes examinations faces several challenges, including an imbalanced number of failing and passing students, a large number of heterogeneous and complex features, and the need to identify at-risk and top-performing students. In this study, two major categories of ML approaches are compared: first, classic models (logistic regression (LR), support vector machine (SVM), and k-nearest neighbors (KNN)), and second, ensemble models (voting, bagging (BG), random forests (RF), adaptive boosting (ADA), extreme gradient boosting (XGB), and stacking).ResultsTo evaluate the models' discrimination ability, they are assessed using a real dataset containing information on medical students over a five-year period (n = 1005). The findings indicate that ensemble ML models demonstrate optimal performance in predicting CMBSE status (RF and stacking). Similarly, among the classic regressors, LR exhibited the highest root-mean-square deviation (RMSD) (0.134) and coefficient of determination (R2) (0.62), whereas the RF model had the highest RMSD (0.077) and R2 (0.80) overall. Furthermore, Anatomical Sciences, Biochemistry, Parasitology, and Entomology grade point average (GPA) and grades demonstrated the strongest positive correlation with the outcomes.ConclusionComparing classic and ensemble ML models revealed that ensemble models are superior to classic models. Therefore, the presented framework could be considered a suitable alternative for the CMBSE and other comparable medical licensing examinations.

Dataset Information

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

Publications

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

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