A Reinforcement Learning-Based Smart Educational Environment for Higher Education

Siyong Fu
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引用次数: 3

Abstract

Most higher education institutions use unique technologies to improve learning activities and provide comfortable learning. Higher education in a smart education environment (SEE) uses various tools and procedures to develop a smart learning environment to improve learning efficiency. Still, these learning processes fail to analyze student knowledge and cognitive features. The inappropriate identification of student learning skills affects teaching and learning quality. This problem is overcome using digital smart classrooms that support the student learning features because social factors and student personal behavior affects learning efficiency. So, the SEE should adapt student variability factors and learning strategies. In this work, reinforcement learning (RL) is utilized to create smart and comfortable learning in a smart classroom. The RL method analyses student behavior change, learning materials, and technologies that improve the overall learning efficiency. The created smart learning classroom achieves benefits of e-learning like interactions, flexibility, and experience.
基于强化学习的高等教育智能教育环境
大多数高等教育机构使用独特的技术来改善学习活动并提供舒适的学习。智能教育环境中的高等教育(SEE)使用各种工具和程序来开发智能学习环境,以提高学习效率。然而,这些学习过程没有分析学生的知识和认知特征。对学生学习技能的不恰当识别影响了教学和学习质量。由于社会因素和学生个人行为会影响学习效率,因此使用支持学生学习特征的数字智能教室可以克服这一问题。因此,SEE应适应学生的可变性因素和学习策略。在这项工作中,强化学习(RL)被用于在智能教室中创造智能和舒适的学习。RL方法分析学生的行为变化、学习材料和提高整体学习效率的技术。创建的智能学习教室实现了电子学习的优点,如交互性、灵活性和体验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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