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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JCER</journal-id><journal-title-group><journal-title>Journal of Contemporary Educational Research</journal-title></journal-title-group><issn>2208-8466</issn><eissn>2208-8474</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jcer.v8i7.7792</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Design and Research of an Intelligent Learning System for University Physics</title><url>https://artdesignp.com/journal/JCER/8/7/10.26689/jcer.v8i7.7792</url><author>ChenLin</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>8</volume><issue>7</issue><history><date date-type="pub"><published-time>2024-07-31</published-time></date></history><abstract>In order to break through the limitations of traditional teaching, realize the integration of online and offline teaching, and optimize the intelligent learning experience of university physics, this paper proposes the design of an intelligent learning system for university physics based on cloud computing platforms, and applies this system to teaching environment of university physics. It successfully integrates emerging technologies such as cloud computing, machine learning, and situational awareness, integrates learning context awareness, intelligent recording and broadcasting, resource sharing, learning performance prediction, and content planning and recommendation, and comprehensively improves the quality of university physics teaching. It can optimize the teaching process and deepen intelligent teaching reform, aiming at providing references for the teaching practice of university physics.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Xiao X, 2022, Reinforcement Learning Optimized Intelligent Electricity Dispatching System. 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