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Big Data in MOOCs

Big Data in MOOCs. Presenters: Nicole Wang, Chad Evans University of Pennsylvania. Our Study. Concentration: Life Cycle of a Million MOOC users Data: 16 Penn Coursera Courses offered between June 2012 and July 2013 Central Finding: Lots of attrition. Four Central Problems.

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Big Data in MOOCs

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  1. Big Data in MOOCs Presenters: Nicole Wang, Chad Evans University of Pennsylvania

  2. Our Study Concentration: Life Cycle of a Million MOOC users Data: 16 Penn Coursera Courses offered between June 2012 and July 2013 Central Finding: Lots of attrition

  3. Four Central Problems Processing Time and Big Data Complicated calculations may cause significant delays in output Analyses will take more time Limited resources available to MOOC researchers Coding introduces particular challenges

  4. Four Central Problems Ambiguous data documentation Examples of variable names ;lkjas;ldf^^^__(*KJNKH_ljllldfkas Transition_in_47_data Challenges working on Secure Servers Frequent Crashing/Cursor Freezing Limitations in copying/pasting No access to the internet and its resources

  5. Tentative solution 1: create a community to collaborate

  6. Tentative solution 2: data dictionary

  7. Tentative solution 3:better data collection/management

  8. Tentative solution 4: communications with tech team

  9. Acknowledgements Research team • Laura Perna, Alan Ruby, Robert Boruch, • Nicole Wang, Janie Scull, Chad Evans, Seher Ahmad Funding • MOOC Research Initiative funded by the Gates Foundation through Athabasca University. • Institute of Education Sciences, U.S. Department of Education, through Grant #R305B90015 to the University of Pennsylvania • Quantitative Methods Division of Penn GSE • Penn AHEAD The opinions expressed are those of the authors and do not represent the views of the funders.

  10. Thanks!Questions? Nicole Wang: ruiw@gse.upenn.edu Chad Evans: echad@sas.upenn.edu

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