Combining Data and Text Mining to Develop an Early Warning System using a Deep Learning Approach

Jennifer Kepka; Jui-Long Hung; Kerry Rice

Combining Data and Text Mining to Develop an Early Warning System using a Deep Learning Approach

Jennifer Kepka, Jui-Long Hung, Kerry Rice

Educational Leadership, Research, and Technology

Boise State University

Research output: Contribution to conference › Presentation

Abstract

Researchers will present findings from a data-mining study analyzing behavioral data from course server logs and asynchronous forum posts from a K-12 virtual learning environment. Using a deep learning approach, our model could predict, with greater accuracy, student success or failure early in the term. Future directions for research inquiry and results will be shared, along with challenges around LMS data storage and analysis, and ways to improve prediction of at-risk students using machine learning.

Original language	American English
State	Published - 26 Oct 2018
Event	2018 Convention of the Association for Educational Communications and Technology - Kansas City, MO Duration: 26 Oct 2018 → …

Conference

Conference	2018 Convention of the Association for Educational Communications and Technology
Period	26/10/18 → …

EGS Disciplines

Educational Technology

Cite this

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title = "Combining Data and Text Mining to Develop an Early Warning System using a Deep Learning Approach",

abstract = " Researchers will present findings from a data-mining study analyzing behavioral data from course server logs and asynchronous forum posts from a K-12 virtual learning environment. Using a deep learning approach, our model could predict, with greater accuracy, student success or failure early in the term. Future directions for research inquiry and results will be shared, along with challenges around LMS data storage and analysis, and ways to improve prediction of at-risk students using machine learning.",

author = "Jennifer Kepka and Jui-Long Hung and Kerry Rice",

year = "2018",

month = oct,

day = "26",

language = "American English",

note = "2018 Convention of the Association for Educational Communications and Technology ; Conference date: 26-10-2018",

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TY - CONF

T1 - Combining Data and Text Mining to Develop an Early Warning System using a Deep Learning Approach

AU - Kepka, Jennifer

AU - Hung, Jui-Long

AU - Rice, Kerry

PY - 2018/10/26

Y1 - 2018/10/26

N2 - Researchers will present findings from a data-mining study analyzing behavioral data from course server logs and asynchronous forum posts from a K-12 virtual learning environment. Using a deep learning approach, our model could predict, with greater accuracy, student success or failure early in the term. Future directions for research inquiry and results will be shared, along with challenges around LMS data storage and analysis, and ways to improve prediction of at-risk students using machine learning.

AB - Researchers will present findings from a data-mining study analyzing behavioral data from course server logs and asynchronous forum posts from a K-12 virtual learning environment. Using a deep learning approach, our model could predict, with greater accuracy, student success or failure early in the term. Future directions for research inquiry and results will be shared, along with challenges around LMS data storage and analysis, and ways to improve prediction of at-risk students using machine learning.

M3 - Presentation

T2 - 2018 Convention of the Association for Educational Communications and Technology

Y2 - 26 October 2018

ER -

Combining Data and Text Mining to Develop an Early Warning System using a Deep Learning Approach

Abstract

Conference

EGS Disciplines

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