DC Field | Value | Language |
---|---|---|
dc.contributor.author | Reza, Mohi | ko |
dc.contributor.author | Kim, Juho | ko |
dc.contributor.author | Bhattacharjee, Ananya. | ko |
dc.contributor.author | Rafferty, Anna N. | ko |
dc.contributor.author | Williams, Joseph Jay | ko |
dc.date.accessioned | 2021-11-04T06:44:50Z | - |
dc.date.available | 2021-11-04T06:44:50Z | - |
dc.date.created | 2021-10-26 | - |
dc.date.created | 2021-10-26 | - |
dc.date.issued | 2021-06 | - |
dc.identifier.citation | 8th Annual ACM Conference on Learning at Scale, L@S 2021, pp.15 - 26 | - |
dc.identifier.uri | http://hdl.handle.net/10203/288791 | - |
dc.description.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. © 2021 ACM. | - |
dc.language | English | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | The MOOClet Framework: Unifying Experimentation, Dynamic Improvement, and Personalization in Online Courses | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85108103174 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 15 | - |
dc.citation.endingpage | 26 | - |
dc.citation.publicationname | 8th Annual ACM Conference on Learning at Scale, L@S 2021 | - |
dc.identifier.conferencecountry | GE | - |
dc.identifier.doi | 10.1145/3430895.3460128 | - |
dc.contributor.localauthor | Kim, Juho | - |
dc.contributor.nonIdAuthor | Reza, Mohi | - |
dc.contributor.nonIdAuthor | Bhattacharjee, Ananya. | - |
dc.contributor.nonIdAuthor | Rafferty, Anna N. | - |
dc.contributor.nonIdAuthor | Williams, Joseph Jay | - |
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