Digital society social interactions and trust analysis model

Kumar, Aseem; Malik, Arun; Batra, Isha; Ahmad, Naveed; Johar, Sumaira

doi:10.7717/peerj-cs.1129

Cited by 1 publication

(1 citation statement)

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“…This algorithm systematically reviews each student's feedback, matching terms, and their synonymous equivalents. The synonymous terms were manually collected and organized into separate files [72,73]. Additionally, part-of-speech (POS) tagging was employed to link specific aspect terms with the related comments, as exemplified in Table 3.…”

Section: Aspects Mappingmentioning

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: Aspects Mappingmentioning

confidence: 99%