Year: 2025
Type: article
Abstract: With the increasing use of e-learning in various fields, there is a growing need to analyse and process big data generated from student interactions with digital learning systems. This data includes test results, content interactions, and learner behavioural data. High dimensionality in data can hinder analysis using AI and machine learning, necessitating dimensionality reduction to enhance model efficiency and reduce computational complexity. The study examines dimensionality reduction techniques like PCA, LDA, autoencoders, and t-SNE in e-learning. It finds traditional methods effective, but advanced methods like deep autoencoders and hybrid AI models offer superior performance. UMAP outperforms t-SNE for clustering and visualisation tasks.
Authors: N. M. A. Munassar, M. A. A. Al-hobishi
Institution: University of Science and Technology
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4412847681