数据驱动下个性化学习资源推荐模式探索

Recommendation Mode of Personalized Learning Resources Based on Data

信息化时代下,学生们往往会遇到着学习资源过多和信息过载的问题。为解决这些问题,本文提出一种个性化学习资源推荐模式。通过收集和整合学生的学习记录、课堂练习成绩和错题记录等数据,并采用结合情感识别技术和个性化学习资源推荐算法,来分析学生的学习行为和知识水平。实验的结果表明,该方法能够提供精准的学习资源推荐,帮助学生更好地掌握智能数据分析的知识和技能,提升学习效果和体验。

In the era of informatization,students often encounter problems of excessive learning resources and information overload.To address these issues,this article proposes a personalized learning resource recommendation model.By collecting and integrating students'learning records,classroom practice scores,and error records,and using a combination of emotion recognition technology and personalized learning resource recommendation algorithms,we analyze students'learning behavior and knowledge level.The experimental results show that this method can provide accurate learning resource recommendations,help students better grasp the knowledge and skills of intelligent data analysis,and improve learning effectiveness and experience.