Activity-centric data gather feedback on students' learning to enhance learning effectiveness. The heterogeneity and multigranularity of such data require existing data models to perform complex on-the-fly computation when responding to queries of specific granularity. This, in turn, results in latency. In addition, existing data models are inefficient in storing computed results, which are often required for follow-up analysis. These follow-up analyses depend largely on stakeholder objectives, which often impose constraints to the analysis process. In this article, we propose a context-based data model that addresses two challenges associated with learning analytics: the increased processing time due to multiple data granularities required from various stakeholder objectives, and the lack of support for archiving and updating of new information that exist during iterative analysis. We demonstrate how the proposed model can help support analysis and visualization via the XuetangX and ASSISTments datasets. Results show that although our proposed data model requires preprocessing, it requires less time to process queries associated with learning-activity attributes of various granularities compared to existing data models.

中文翻译：

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基于上下文的数据模型，用于有效的实时学习分析


以活动为中心的数据收集学生学习的反馈，以提高学习效率。此类数据的异构性和多粒度要求现有数据模型在响应特定粒度的查询时执行复杂的动态计算。这反过来又会导致延迟。此外，现有的数据模型在存储计算结果方面效率低下，而后续分析通常需要这些结果。这些后续分析在很大程度上取决于利益相关者的目标，这通常会对分析过程施加限制。在本文中，我们提出了一种基于上下文的数据模型，该模型解决了与学习分析相关的两个挑战：由于不同利益相关者目标所需的多个数据粒度而导致处理时间增加，以及缺乏对现有新信息的归档和更新的支持在迭代分析过程中。我们演示了所提出的模型如何通过 XuetangX 和 ASSISTments 数据集帮助支持分析和可视化。结果表明，尽管我们提出的数据模型需要预处理，但与现有数据模型相比，处理与各种粒度的学习活动属性相关的查询所需的时间更少。