Big Data Mining Using K-Means and DBSCAN Clustering Techniques

Omer, Albasheer Fawzia; Mohammed, Hamdy; Awadallah, M. Ahmed; Khan, Zia U.; Abrar, Said Ul; Shah, Mian Dawood

doi:10.1007/978-3-031-05752-6_15

Cited by 4 publications

(1 citation statement)

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“…In pattern discovery's second phase, we uncover concealed insights and patterns within the processed data. Data mining and machine learning approaches are used to accomplish this; in the final stage, known as pattern analysis [31], our attention is directed toward extracting noteworthy and compelling patterns from the outcomes of the pattern discovery phase. Finally, the learning sequences of learners are generated; learning sequences refer to learners' way of learning.…”

Section: Web Usage Mining Techniquesmentioning

confidence: 99%

Enhancing E-Learning Adaptability with Automated Learning Style Identification and Sentiment Analysis: A Hybrid Deep Learning Approach for Smart Education

Hussain,

Yu,

Asim

et al. 2024

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In smart education, adaptive e-learning systems personalize the educational process by tailoring it to individual learning styles. Traditionally, identifying these styles relies on learners completing surveys and questionnaires, which can be tedious and may not reflect their true preferences. Additionally, this approach assumes that learning styles are fixed, leading to a cold-start problem when automatically identifying styles based on e-learning platform behaviors. To address these challenges, we propose a novel approach that annotates unlabeled student feedback using multi-layer topic modeling and implements the Felder–Silverman Learning Style Model (FSLSM) to identify learning styles automatically. Our method involves learners answering four FSLSM-based questions upon logging into the e-learning platform and providing personal information like age, gender, and cognitive characteristics, which are weighted using fuzzy logic. We then analyze learners’ behaviors and activities using web usage mining techniques, classifying their learning sequences into specific styles with an advanced deep learning model. Additionally, we analyze textual feedback using latent Dirichlet allocation (LDA) for sentiment analysis to enhance the learning experience further. The experimental results demonstrate that our approach outperforms existing models in accurately detecting learning styles and improves the overall quality of personalized content delivery.

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Section: Web Usage Mining Techniquesmentioning

confidence: 99%