Understanding student behaviors in online classroom: data scientific approach

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

doi:10.1109/BigData.Congress.2014.129

Understanding student behaviors in online classroom: data scientific approach

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

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

27 Downloads (Pure)

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 language	English
Pages	802-803
Number of pages	2
DOIs	https://doi.org/10.1109/BigData.Congress.2014.129
Publication status	Published - 25 Sept 2014
Event	3rd IEEE International Congress on Big Data, BigData Congress 2014 - Washington, DC, United States Duration: 27 Jun 2014 → 2 Jul 2014

Conference

Conference	3rd IEEE International Congress on Big Data, BigData Congress 2014
Country/Territory	United States
City	Washington, DC
Period	27/06/14 → 2/07/14

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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10.1109/BigData.Congress.2014.129

Byan-etal-BigData-2014-Understanding-student-behaviors-in-online-classromAccepted author manuscript, 317 KB

Cite this

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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.",

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AU - Byun, Jongbok

AU - Pennington, Diane

AU - Cardenas, Jorge

AU - Dutta, Srabasti

AU - Kirwan, Jeral

N1 - © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2014/9/25

Y1 - 2014/9/25

N2 - 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.

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KW - Big Data

KW - Data Science

KW - online education

KW - students Behavior

KW - distance education

KW - Drops

KW - education

KW - social networking (online)

KW - teaching

KW - student learning

KW - students' behaviors

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M3 - Paper

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Y2 - 27 June 2014 through 2 July 2014

ER -

Understanding student behaviors in online classroom: data scientific approach

Abstract

Conference

Keywords

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