moooclet框架:在线课程的统一实验、动态改进和个性化

Mohi Reza, Juho Kim, Ananya Bhattacharjee, Anna N. Rafferty, J. Williams
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引用次数: 10

摘要

如何利用教育平台加速利用研究成果来改善学生的体验?我们展示了任何教育界面的模块化组件-例如解释,作业问题,甚至电子邮件-可以使用新颖的MOOClet软件架构实现。研究人员和教师可以使用这些增强的moolet组件进行:(1)随机实验的迭代循环,测试课程内容的替代版本;(2)数据驱动的改进,使用适应性实验,快速使用数据为未来的学生提供更好的内容版本,以天而不是以月为单位。moolet支持使用强化学习进行手动和自动改进;(3)个性化,通过使用专家撰写的规则和数据挖掘算法,提供关于学生特征或子群体的数据函数的替代版本。我们提供了一个开源的网络服务来实现moolet (www.mooclet.org),已经有成千上万的学生使用了这个服务。moolet框架提供了一个生态系统,将在线课程组件转化为协作微型实验室,在这里,教师、实验研究人员和数据挖掘/机器学习研究人员可以参与实验、改进和个性化的永久循环。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The MOOClet Framework: Unifying Experimentation, Dynamic Improvement, and Personalization in Online Courses
How can educational platforms be instrumented to accelerate the use of research to improve students' experiences? We show how modular components of any educational interface - e.g. explanations, homework problems, even emails - can be implemented using the novel MOOClet software architecture. Researchers and instructors can use these augmented MOOClet components for: (1) Iterative Cycles of Randomized Experiments that test alternative versions of course content; (2) Data-Driven Improvement using adaptive experiments that rapidly use data to give better versions of content to future students, on the order of days rather than months. A MOOClet supports both manual and automated improvement using reinforcement learning; (3) Personalization by delivering alternative versions as a function of data about a student's characteristics or subgroup, using both expert-authored rules and data mining algorithms. We provide an open-source web service for implementing MOOClets (www.mooclet.org) that has been used with thousands of students. The MOOClet framework provides an ecosystem that transforms online course components into collaborative micro-laboratories, where instructors, experimental researchers, and data mining/machine learning researchers can engage in perpetual cycles of experimentation, improvement, and personalization.
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