Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics · Innovative Teaching and Learning Methods · Intelligent Tutoring Systems and Adaptive Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Shaanxi Institute of Technology, Xi’an 710300, China
School of Computer Science and Engineering, Kunsan National University, Gunsan 54150, Republic of Korea
Department of Digital Contents Engineering, Wonkwang University, Iksan 54538, Republic of Korea
Funding
MISP (Ministry of Science, ICT & Future Planning), Korea
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