Research on Personalized Service Path of Learning Resources by Data Driven

Haonan Wang, Ziqing Gao
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Abstract

In the digital age, the trend of personalized learning requires learning resources to provide learners with more accurate and intelligent personalized services. However, current research is inadequate in meeting the need for personalized, intelligent, and accurate digital learning resource services for learners. This study is based on a large number of learning analytics and learning resources relevant to the analysis of literature studies and the corresponding theoretical framework, using over 1.4 million learner data collected and stored by H Publishing from real-life contexts and natural learning conditions as the basis of the study. This study uses learning analytics and artificial intelligence technologies to analyze learners' personalized learning needs and construct intelligent adaptive service path for learning resources. On the one hand, it reduces the unreasonable use of digital learning resources, saves learner's time costs, and improves learning efficiency. On the other hand, it provides effective references for digital learning resources can better serve learners.
数据驱动下的学习资源个性化服务路径研究
数字化时代,个性化学习的趋势要求学习资源为学习者提供更加精准、智能的个性化服务。然而,目前的研究还不足以满足学习者个性化、智能化、精准化的数字化学习资源服务需求。本研究以大量的学习分析和与文献研究分析相关的学习资源以及相应的理论框架为基础,以H Publishing从现实情境和自然学习条件中收集和存储的140多万学习者数据为研究基础。本研究利用学习分析和人工智能技术,分析学习者的个性化学习需求,构建学习资源的智能自适应服务路径。一方面减少了数字化学习资源的不合理使用,节省了学习者的时间成本,提高了学习效率。另一方面,为数字化学习资源更好地为学习者服务提供了有效的参考。
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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