泛化高度可配置的分析管道以跨多个领域复制和支持教育研究
Generalizing a Highly Configurable Analytics Pipeline to Replicate and Support Educational Research Across Multiple Domains
AI总结：
提出A4L数据分析管道的模块化设计，通过案例研究证明其能跨不同领域一致应用统计分析方法并扩展分析能力。
摘要：
部署在在线学习环境中的人工智能助手创造了收集大量学习者交互数据的新机会，并生成改善学生成果的见解。AI增强学习架构（A4L）是一种模块化数据架构，能够收集、整合和分析来自教育AI系统的学习者交互数据，支持生成教学见解，促进个性化学习并加强教师与学习者之间的双向反馈循环。本研究考察了A4L数据分析管道的模块化设计，这是一种可扩展的数据基础设施，能够摄取、处理和分析由教育AI助手生成的异构数据集。我们描述了用于扩展管道分析能力同时保持跨领域灵活性的设计原则和开发过程。我们通过涵盖三个研究领域的案例研究评估该管道，这些领域对应于佐治亚理工学院在线学习环境中部署的三个教育AI助手。结果表明，一组通用的统计分析方法可以一致地应用于具有不同结构和教学背景的数据集，使管道能够跨领域复现关键分析结果。我们展示了最初为一个领域开发的分析能力如何扩展以支持另一个领域更丰富的分析，说明了管道的可扩展性。这些发现表明，A4L分析管道可以作为可重用的基础设施，用于分析未来教育AI助手生成的数据。通过启用可以系统地扩展到新领域的分析，该管道为推导用于指导教育AI系统设计和评估的见解提供了基础。
英文摘要：
Artificial intelligence assistants deployed in online learning environments create new opportunities to collect large volumes of learner interaction data and generate insights to improve student outcomes. Architecture for AI-Augmented Learning (A4L) is a modular data architecture that enables the collection, integration, and analysis of learner interaction data from educational AI systems, supporting the generation of instructional insights that facilitate personalized learning and reinforce the bidirectional feedback loop between instructors and learners. This study examines the modular design of the A4L Data Analytics Pipeline, an extensible data infrastructure that enables the ingestion, processing, and analysis of heterogeneous datasets generated by educational AI assistants. We describe the design principles and development process used to extend the pipeline's analytical capabilities while preserving flexibility across domains. We evaluate the pipeline through case studies spanning three research domains corresponding to three educational AI assistants deployed in online learning environments at Georgia Tech. Results show that a common set of statistical analysis methods can be consistently applied across datasets with differing structures and instructional contexts, enabling the pipeline to reproduce key analytical findings across domains. We demonstrate how analytical capabilities initially developed for one domain can be extended to support richer analyses in another, illustrating the pipeline's extensibility. These findings suggest that the A4L Analytics Pipeline can serve as reusable infrastructure for analyzing data generated by future educational AI assistants. By enabling analytics that can be systematically extended to new domains, the pipeline provides a foundation for deriving insights that inform the design and evaluation of educational AI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
