The assessment of learning infrastructure (ALI): the theory, practice, and scalability of automated assessment
The assessment of learning infrastructure (ALI): the theory, practice, and scalability of automated assessment
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学习基础设施评估（ALI）：自动评估的理论、实践和可扩展性
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
J. Williams
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文献类型：
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作者：
Korinn S. Ostrow;Douglas Selent;Yan Wang;E. V. Inwegen;N. Heffernan;J. Williams
投资于K-12教育的研究人员不仅要努力提高教学法，课程和学生参与度，还要利用技术的力量来优化学习。在线学习平台为大规模的教育研究提供了一个强大的环境。目前的工作详细介绍了一个自动化系统的创建，旨在为研究人员提供有关在ASSISTments TestBed内进行的随机对照实验记录的数据的见解。学习基础设施评估（ALI）建立在现有技术的基础上，以促进学生，研究人员，平台及其内容以及学习分析社区的共生关系。ALI是一个复杂的自动报告系统，它提供了样本分布和基本分析的概述，供研究人员在评估数据时考虑。ALI的好处也可以通过横切多项研究的分析来大规模感受，以推动迭代平台的改进，同时促进个性化学习。
Researchers invested in K-12 education struggle not just to enhance pedagogy, curriculum, and student engagement, but also to harness the power of technology in ways that will optimize learning. Online learning platforms offer a powerful environment for educational research at scale. The present work details the creation of an automated system designed to provide researchers with insights regarding data logged from randomized controlled experiments conducted within the ASSISTments TestBed. The Assessment of Learning Infrastructure (ALI) builds upon existing technologies to foster a symbiotic relationship beneficial to students, researchers, the platform and its content, and the learning analytics community. ALI is a sophisticated automated reporting system that provides an overview of sample distributions and basic analyses for researchers to consider when assessing their data. ALI's benefits can also be felt at scale through analyses that crosscut multiple studies to drive iterative platform improvements while promoting personalized learning.