The MOOClet Framework: Unifying Experimentation, Dynamic Improvement, and Personalization in Online Courses

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

Abstract

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.
moooclet框架:在线课程的统一实验、动态改进和个性化
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。
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