ScholarOne - Unlocking Cognitive Patterns: A Comparative Exploration of Linear and Deep Dimensionality Reduction Approaches in clusterizing Student Cognitive Profiles.

Cognitive profiling plays a crucial role in understanding learning dynamics, it contributes significantly to the development of students’ metacognitive skills and awareness of the learning process, thereby facilitating the adoption of tailored learning experiences. Clustering, proves effective in cognitive profiling. However, the challenge of the “curse of dimensionality” introduces complexities that can impact the accuracy of cluster subject attribution. This paper investigates the evaluation of various cluster internal validation metrics and cluster stability using a dataset of 1626 participants comprising 54 items across six cognitive domains from the digital assessment tool, PROFFILO. We employ three clustering procedures—K-means, Gaussian Mixture Models, and Fuzzy-C Means—on raw data and apply linear (Principal Component Analysis) or non-linear (Variational Autoencoders), or a combination of PCA and VAE dimensionality reduction techniques. Results indicate that, for high-dimensional cognitive domains, a combination of PCA and VAE yields superior clustering quality. Conversely, in less high-dimensional domains, the VAE outperforms the PCA approach. In summary, the application of dimensionality reduction techniques demonstrates promising outcomes in student cognitive profiling, especially for data characterized by high dimensionality and heterogeneity. These findings have practical implications for advancing personalized learning experiences and enhancing our understanding of the intricate relationships within students’ cognitive domains.