Understanding student behaviors in online classroom: data scientific approach

Jongbok Byun, Diane Pennington, Jorge Cardenas, Srabasti Dutta, Jeral Kirwan

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Abstract

Students drop classes for many reasons. Some are personal such as medical conditions, family issues, or financial difficulties. Others are course specific such as course contents, instructor, or classmates. In either way, class drop is a serious problem to institutions because without students there will be no students learning. As a first step to understand students drops, this study will address the issue of students' behaviors in online classrooms.
Original languageEnglish
Pages802-803
Number of pages2
DOIs
Publication statusPublished - 25 Sep 2014
Event3rd IEEE International Congress on Big Data, BigData Congress 2014 - Washington, DC, United States
Duration: 27 Jun 20142 Jul 2014

Conference

Conference3rd IEEE International Congress on Big Data, BigData Congress 2014
CountryUnited States
CityWashington, DC
Period27/06/142/07/14

Keywords

  • Big Data
  • Data Science
  • online education
  • students Behavior
  • distance education
  • Drops
  • education
  • social networking (online)
  • teaching
  • student learning
  • students' behaviors

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  • Cite this

    Byun, J., Pennington, D., Cardenas, J., Dutta, S., & Kirwan, J. (2014). Understanding student behaviors in online classroom: data scientific approach. 802-803. Paper presented at 3rd IEEE International Congress on Big Data, BigData Congress 2014, Washington, DC, United States. https://doi.org/10.1109/BigData.Congress.2014.129