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基于贝叶斯分类的计算机类专业线上课程资源整合

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随着计算机科学教育的线上化发展,有效整合线上课程资源成为提升教学质量的关键挑战,研究构建基于贝叶斯分类的模型,实现计算机类专业线上课程资源的优化整合.分析学生的学习行为数据,结合教学资源的动态特征来预测学生对不同资源的偏好,并据此提供个性化的资源推荐,通过这种方法加强学生的学习体验,提高资源利用效率,并最终促进学习成果的提升,为线上计算机科学教育领域的教学资源管理提供理论支持.
Online Course Resources Integration for Computer Majors Based on Bayesian Classification
With the online development of computer science education, effectively integrating online course resources has become a key challenge to improve teaching quality. A model based on Bayesian classification is researched and constructed to realize the optimization and integration of online course resources for computer majors. By analyzing students' learning behavior data and combining with the dynamic characteristics of teaching resources, students' preferences for different resources are predicted, and personalized resource recommendations are provided accordingly. Through this method, students' learning experience is enhanced, resource utilization efficiency is improved, and learning outcomes are ultimately promoted, providing theoretical support for teaching resource management in the field of online computer science education.

computer educationBayesian classificationonline resource integrationpersonalized recommendations

卢爱芬

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广州科技职业技术大学,广东广州 510550

计算机教育 贝叶斯分类 线上资源整合 个性化推荐

广东教育学会教育科研课题(2023)广东高教学会规划高等教育研究课题(十四五)(2023)

GDES1443523GYB116

2024

湖南邮电职业技术学院学报
长江通信职业技术学院

湖南邮电职业技术学院学报

影响因子:0.424
ISSN:2095-7661
年,卷(期):2024.23(1)
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