Show simple item record

dc.contributor.advisorAna Bell.en_US
dc.contributor.authorAbadiotakis, Helen.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2021-01-05T23:11:26Z
dc.date.available2021-01-05T23:11:26Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/128986
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020en_US
dc.descriptionCataloged from student-submitted PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (page 57).en_US
dc.description.abstractThe ever-increasingly relevant introductory programming course offered at MIT presents a unique opportunity to uncover student learning patterns and common behavioral motifs. The course 6.0001/6.0002 harbors a wealth of student interaction data on its companion MITx platform as well as associated grades. Although this course has been offered for the last twelve years, since 2008, little has been done to identify aspects of the course that best aid or hinder student success. This thesis will focus on finding various learner subpopulations to elucidate those materials that best aid certain students to allow for a more tailored teaching mode for future iterations of the course. In addition, this thesis will define an 'effort' statistic that encompasses the holistic engagement of a given student in order to provide an additional statistic to use when determining final grades. I begin with a course specific analysis of enrollment demonstrating the significance of this type of analysis.en_US
dc.description.abstractGiven enrollment numbers that rival a general institute requirement, this analysis could easily extend its finding to these other large courses. In addition, I show how this course, by utilizing the MITx platform, best leverages a way to facilitate student introduction to a programming language. Second, I look to see how the introduction of an 'effort' statistic would positively affect grading outcomes for certain students near a letter grade border. I identify a possible mode to utilize this index during the determination of final grading as an additional measure in order to improve the issued letter grade. Lastly, curious to see generalizability of results, sought out to survey past students to see if their computed effort statistic aligned with their personal view of effort into the course. I furthermore use these findings to tabulate resources that are most helpful to student subpopulations.en_US
dc.description.abstractCollectively, the inquiries in this thesis form a foundation for a more equitable way of teaching where students are best equipped for success.en_US
dc.description.statementofresponsibilityby Helen Abadiotakis.en_US
dc.format.extent57 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleIdentifying patterns of learning : a case study of MIT's Introductory Programming Course (6.000x)en_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1227095495en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2021-01-05T23:11:26Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record