A Learning Social Network with Recognition of Learning Styles Using Neural Networks

Zataraín-Cabada, Ramón; Barrón-Estrada, M. L.; Angulo, Viridiana Ponce; García, Adán José; García, Catalina Beatriz García

doi:10.1007/978-3-642-15992-3_22

Cited by 12 publications

(4 citation statements)

References 9 publications

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“…In [12], the authors integrated fuzzy logic with neural networks to train an algorithm capable of recognizing various learning styles. However, the algorithm's effectiveness was limited to classifying just three dimensions of the FSLSM model: perception, input, and understanding.…”

Section: Related Workmentioning

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: Related Workmentioning

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

Information

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show abstract

“…Lo and Shu (2005), Villaverde et al (2006), Alfaro et al (2018, and Bajaj and Sharma (2018) use as input the completed questionnaire and/or other data sources such as interaction data and behavioral data of students, and feed the extracted features into feed-forward neural networks for classification. Unsupervised methods such as self-organizing map (SOM) trained using curated features have also been used for automatic learning style identification (Zatarain-Cabada et al, 2010). While for categorization per the Felder and Silverman learning style model, count of student visits to different sections of the e-learning platform are found to be more informative (Bernard et al, 2015;Bajaj and Sharma, 2018), for categorization per the Kolb learning model, student performance, and student preference features were found to be more relevant.…”

Section: Reactive Engagement Of Ai For Education Tutoring Aidsmentioning

confidence: 99%

Proactive and reactive engagement of artificial intelligence methods for education: a review

Mallik

Gangopadhyay

2023

Front. Artif. Intell.

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The education sector has benefited enormously through integrating digital technology driven tools and platforms. In recent years, artificial intelligence based methods are being considered as the next generation of technology that can enhance the experience of education for students, teachers, and administrative staff alike. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled these efforts further. In this review article, we investigate how artificial intelligence, machine learning, and deep learning methods are being utilized to support the education process. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety—from students admissions, course scheduling, and content generation in the proactive planning phase to knowledge delivery, performance assessment, and outcome prediction in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 195 original research articles published in the past two decades, i.e., 2003–2022. We discuss the paradigm shifts in the solution approaches proposed, particularly with respect to the choice of data and algorithms used over this time. We further discuss how the COVID-19 pandemic influenced this field of active development and the existing infrastructural challenges and ethical concerns pertaining to global adoption of artificial intelligence for education.

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“…Zamná (Zatarain-Cabada et al, 2010) funciona bajo un ambiente de operación web 2.0. Esta plataforma de aprendizaje reúne las ventajas de ser adaptativa e inteligente, además de operar dentro de un contexto de inteligencia colectiva que establece un esquema donde los usuarios son los generadores de su propio conocimiento.…”

Section: ¿Qué Es Zamná?unclassified

Sistemas inteligentes aplicados a la educación

Zataraín-Cabada¹,

Barrón-Estrada²,

Figueroa³

2023

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En esta obra se presentan conceptos y temas básicos de algunas áreas de conocimiento necesarias para desarrollar sistemas computacionales inteligentes aplicados a la educación, así como su implementación en cuatro trabajos de investigación que incorporan diferentes técnicas y módulos inteligentes (lógica difusa, algoritmos genéticos, o redes neuronales) con el fin de proporcionar al usuario una experiencia de aprendizaje personalizada más adecuada a sus necesidades cognitivas y afectivas.

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A Learning Social Network with Recognition of Learning Styles Using Neural Networks

Cited by 12 publications

References 9 publications

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

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

Proactive and reactive engagement of artificial intelligence methods for education: a review

Sistemas inteligentes aplicados a la educación

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