【摘要】 学习分析是指在教学活动中分析教和学活动的过程，是面向未来智能教育跨学科培养人才的关键技术。当前，如何通过对学习环境和学习活动数据的收集和分析，为学生提供新的学习机会，引起教育界前所未有的关注。而人工智能的迅速发展和在教育中的广泛运用，为以学习者为中心的数据分析提供了新的机会和挑战，也为解决教育教学中的许多棘手问题提供了新的可能。文章回顾了近几年国际上学习分析研究和应用方面所面临的挑战和主要成就；探讨未来学习分析发展中，如何从人工智能等相关技术和领域的研究中受益；剖析学习分析框架中数据收集、模型建立及实践应用中的关键问题；并运用大量的实证研究案例，展示学习分析在自主学习、发展学习策略、解决个人和小组合作学习评价问题中的应用潜力。在此基础上，文章从数据使用和获取、模型优化和实施、模型可解释性、学术研究四个层面，提出未来学习分析的研究建议。更多还原

【Abstract】 Learning analytics is the process of analyzing teaching and learning activities. It is a key technology of interdisciplinary talent training for future intelligent education. Recently, data collection, analysis of learning environments and learning activities have attracted enormous attention in the educational field. The rapid development of artificial intelligence technology and its wide application in the educational field have provided opportunities for learning-centered data analysis, and it provides a new way to solve many thorny problems in education. This paper reviews the challenges and achievements of learning analytics in recent years; also it discusses how the future development of learning analytics can benefit from artificial intelligence;analyzing the key issues of the learning analytics framework in data collection, modeling and transformation. Finally, a number of empirical cases are used to demonstrate the application and potential of learning analytics in self-regulated learning, learning strategies, solving learning problems between individual and cooperative. Furthermore, the paper suggests four areas of research for future learning analytics, namely: data use and acquisition, model optimization and implementation, model explainability, and academic research.更多还原