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dc.contributor.advisorKalyan Veeramachaneni.en_US
dc.contributor.authorHelbert, Justin (Justin C.)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2016-12-22T16:30:03Z
dc.date.available2016-12-22T16:30:03Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/106126
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (page 51).en_US
dc.description.abstractMeasuring the effect of a course component in online classes present an opportunity to use propensity score methods. Propensity score methods aim to balance the effect of self-selecting biases and other confounding variables that arise in observational studies like this, as each student decides what components they engage in throughout the course. This method is applied to an edX course, 6.002x, to estimate the effect of attempting homework and other assessments on students' final exam performance.en_US
dc.description.statementofresponsibilityby Justin Helbert.en_US
dc.format.extent54 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleMethods for observational studies using data from massive open online coursesen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc965831206en_US


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