A Fuzzy-Neural Model for Personalized Learning Recommendations Grounded in Experiential Learning Theory DOI Creative Commons
Christos Troussas, Akrivi Krouska, Phivos Mylonas

et al.

Information, Journal Year: 2025, Volume and Issue: 16(5), P. 339 - 339

Published: April 23, 2025

Personalized learning is a defining characteristic of current education, with flexible and adaptable experiences that respond to individual learners’ requirements approaches learning. Traditional implementations educational theories—such as Kolb’s Experiential Learning Theory—often follow rule-based approaches, offering predefined structures but lacking adaptability dynamically changing learner behavior. In contrast, AI-based such artificial neural networks (ANNs) have high lack interpretability. this work, new model, fuzzy-ANN developed combines fuzzy logic ANNs make recommendations for activities in the process, overcoming model weaknesses. first stage, used map dimensions style onto continuous membership values, providing easier-to-interpret representation preferred These weights are then processed an ANN, enabling refinement improvement through analysis patterns To adapt develop over time, Weighted Sum Model (WSM) used, combining activity trends real-time feedback updating proposed recommendations. Experimental evaluation environment shows effectively generates personalized learners, harmony trends.

Language: Английский

A Fuzzy-Neural Model for Personalized Learning Recommendations Grounded in Experiential Learning Theory DOI Creative Commons
Christos Troussas, Akrivi Krouska, Phivos Mylonas

et al.

Information, Journal Year: 2025, Volume and Issue: 16(5), P. 339 - 339

Published: April 23, 2025

Personalized learning is a defining characteristic of current education, with flexible and adaptable experiences that respond to individual learners’ requirements approaches learning. Traditional implementations educational theories—such as Kolb’s Experiential Learning Theory—often follow rule-based approaches, offering predefined structures but lacking adaptability dynamically changing learner behavior. In contrast, AI-based such artificial neural networks (ANNs) have high lack interpretability. this work, new model, fuzzy-ANN developed combines fuzzy logic ANNs make recommendations for activities in the process, overcoming model weaknesses. first stage, used map dimensions style onto continuous membership values, providing easier-to-interpret representation preferred These weights are then processed an ANN, enabling refinement improvement through analysis patterns To adapt develop over time, Weighted Sum Model (WSM) used, combining activity trends real-time feedback updating proposed recommendations. Experimental evaluation environment shows effectively generates personalized learners, harmony trends.

Language: Английский

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