Learning Style Identification Using Semisupervised Self-Taught Labeling

Ayyoub, Hani Y.; Al-Kadi, Omar S.

doi:10.1109/tlt.2024.3358864

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…The other models, like the "Robust Fuzzy C-Means algorithm (RFCM)" [77], "Category Boosting (Catboost)", and "Extreme Gradient Boosting (XGBoost) [78]", show commendable accuracies, all above 92%, but they are not quite as precise as the proposed model. Models with lower accuracies, like the "K-modes Naive Bayes Learning Styles (KM-NB-LS)" [79], "Adaptive Fuzzy C-Mean model (AFCM)" [80], and "Self-Taught Semi-Supervised Learning (ST-SSL)" [81], hover around 88-89%, which might suggest a less sensitive approach to the subtleties of learning styles. In the bar chart for sentiment analysis of learner feedback shown in Figure 16, the proposed model's top performance is consistent, registering an accuracy of 93.81%.…”

Section: Discussion and Comparisonmentioning

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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