Inicio  /  Applied Sciences  /  Vol: 14 Par: 2 (2024)  /  Artículo
ARTÍCULO
TITULO

Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways

Chuanxiang Song    
Seong-Yoon Shin and Kwang-Seong Shin    

Resumen

This study introduces a novel approach named the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), aimed at personalizing educational pathways through machine learning technology. Our findings reveal that this framework significantly enhances student engagement and learning effectiveness by providing real-time feedback and personalized instructional content tailored to individual learning needs. This research demonstrates the potential of leveraging advanced technology to create more effective and individualized learning environments, offering educators a new tool to support each student?s learning journey. The study thus contributes to the field by showcasing how personalized education can be optimized using modern technological advancements.

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