Article
Version 1
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AI-Powered Educational Data Analysis for Early Identification of Learning Difficulties
Version 1
: Received: 2 August 2024 / Approved: 4 August 2024 / Online: 6 August 2024 (05:15:51 CEST)
How to cite: Wang, C.; Zou, J.; Xie, Z. AI-Powered Educational Data Analysis for Early Identification of Learning Difficulties. Preprints 2024, 2024080226. https://doi.org/10.20944/preprints202408.0226.v1 Wang, C.; Zou, J.; Xie, Z. AI-Powered Educational Data Analysis for Early Identification of Learning Difficulties. Preprints 2024, 2024080226. https://doi.org/10.20944/preprints202408.0226.v1
Abstract
Based on the detailed exploration of AI techniques for the early identification of learning difficulties in education, this study underscores the pivotal role of educational data analysis in enhancing student outcomes. Educators can proactively identify and address learning challenges through systematic data collection and analysis, thereby minimizing long-term academic obstacles and emotional stress. The integration of AI, particularly through natural language processing and computer vision technologies, facilitates personalized learning paths and real-time interventions, offering tailored support that maximizes each student's learning potential. These advancements optimize educational resource allocation and foster a more inclusive and effective learning environment, paving the way for future advancements in personalized education and intelligent teaching practices.
Keywords
Early Identification of Learning Difficulties; Artificial Intelligence in Education; Personalized Learning Paths; Real-time Feedback Systems
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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