大数据时代运用学习分析技术挖掘教育数据背后的知识，优化学习过程成为教育者的共同诉求。学习分析仪表盘实现了知识生成与教育数据挖掘结果可视化，能够支持学生自我认知、自我评价、自我激励和社会意识及未来智慧学习环境。该文采用文献分析法对国内外学习分析仪表盘发展现状进行了梳理，综述了10个学习分析仪表盘案例并分析其特性，实现通过教育数据挖掘技术和可视化技术支持学生和教师。基于Few仪表盘设计原则和Kirkpatrick四层评价模型设计学习分析仪表盘概念框架，并从个人、他人及个人与班级等视角设计了学习分析仪表盘。最后，以美国匹兹堡大学PAWS中心研发的Mastery Grids自适应学习系统为例，采用实验研究法、问卷调查及访谈等方法对学习分析仪表盘进行主观、客观评价。研究结果表明尽管学习分析仪表盘对学习成绩促进作用不显著，但却从本质上提高了学习效率和动机，增强了学生对学习的认知度和对课程学习的满意度。%To mine the knowledge behind the education data and optimize learning process using learning analytics technology has been the shared desire in big data era. A learning analytics dashboard is a display which visualizes the results of educational data mining in a useful way, supports students’ self-knowledge, self-evaluation, self-motivation, social awareness and smart learning environments. Based on a literature review of the related researches, this paper analyzes learning analytics dashboard development and compares with 10 related dashboard applications for learning and analyzes their features, for supporting students and teachers through educational data mining techniques and visualization technologies. A conceptual framework based on Few’s principles of dashboard design and Kirkpatrick’s four-level evaluation model has been developed to review learning analytics dashboard which has been designed from a personal, the others and class perspective. Finally, taking a Mastery Grids which has been developed by PAWS in University of Pittsburgh, for example, learning analytics dashboard has been evaluated using experimental methodology, questionnaire and interview method. The results show that although learning analytics dashboard doesn’t signiifcantly impact on their learning achievement, it has improved learning effciency, motivation, and enhanced learning cognition and satisfaction.
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