【作 者】：
丁继红，刘华中
【关 键 词 】：
精准推荐；大数据；个性化学习；多维关联分析
【栏 目】：
学习环境与资源
【中文摘要】：
从教育大数据中为学习者提供个性化学习资源是缓解学习迷航、提升在线学习体验的有效途径。为实现精准的学习资源推荐，关键要整体考虑数据之间的复杂关系，对学习者、资源、情境等进行多维关联分析。本研究引入张量理论构建“学习者—资源”融合张量，依据多维关联分析原理，采用高阶奇异值分解算法挖掘学习者和资源的关联关系，实现学习者和资源之间的精准匹配。通过9组不同规模数据的对比实验，发现多维关联分析方法的推荐性能均优于在推荐领域成熟应用的协同过滤算法，并且随着数据规模增大，其推荐性能愈优。多维关联分析方法有利于大数据环境下个性化学习资源精准推荐，提升在线教育质量和个性化学习效果。
【英文摘要】：
Providing personalized learning resources for learners from educational big data is an effective way to reduce learning disorientation and improve learners' online learning experience. The key of realizing accurate learning resource recommendation is to excavate and analyze complicated relationships among the data, including learners, resources, situations, etc. Therefore, tensor theory is introduced to construct a learner-resource fusion tensor. Afterwards, according to the principle of multi-dimensional correlation analysis, the high order singular value decomposition approach is adopted to mine the associations of learners and resources to realize the accurate matching between them. Finally, a contrast experiment is conducted based on 9 groups of data with different scales. The experimental results show that the recommendation performance of multi-dimensional correlation analysis method outperforms that of the collaborative filtering approach maturely applied to the recommendation field, and the larger the data scale becomes, the better the recommendation performance. It is implied that the multi-dimensional correlation analysis method is beneficial to the accurate recommendation of personalized learning resources in age of big data, to improving the quality of online education and enhancing personalized learning effect.