Web-Based Analysis of Student Activity for Predicting Dropout

Anat Cohen

Web-Based Analysis of Student Activity for Predicting Dropout

Anat Cohen

School of Education

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Persistence in learning processes is perceived as a central value in education (Horowitz, 1992); therefore, dropout from studies is a prime concern for educators (Barefoot, 2004). Since the increase in student usage of online learning materials on course websites, as well as online courses (Allen & Seaman, 2014; Parker, Lenhart, & Moore, 2011), it is essential to address the dropout issue in a wide array of configurations from web-supported learning to fully online courses. Additional tools and strategies must be developed to allow instructors or other educational decision makers to quickly identify at-risk students and find ways to support their learning in the early stages, before they actually drop out. The ability to detect these students during the semester, and not at the end of the course, can also serve as a basis for the development of appropriate assistance mechanisms which will enable those students to complete the course and even to fulfil the curriculum for their degree

Original language	English
Title of host publication	Proceedings of the European Distance and E-Learning Network 2015 Annual Conference
Subtitle of host publication	Expanding Learning Scenarios Opening Out the Educational Landscape
Place of Publication	Barcelona
Number of pages	11
State	Published - 2015

Publication series

Name	European Distance and E-Learning Network

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education

Cite this

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title = "Web-Based Analysis of Student Activity for Predicting Dropout",

abstract = "Persistence in learning processes is perceived as a central value in education (Horowitz, 1992); therefore, dropout from studies is a prime concern for educators (Barefoot, 2004). Since the increase in student usage of online learning materials on course websites, as well as online courses (Allen \& Seaman, 2014; Parker, Lenhart, \& Moore, 2011), it is essential to address the dropout issue in a wide array of configurations from web-supported learning to fully online courses. Additional tools and strategies must be developed to allow instructors or other educational decision makers to quickly identify at-risk students and find ways to support their learning in the early stages, before they actually drop out. The ability to detect these students during the semester, and not at the end of the course, can also serve as a basis for the development of appropriate assistance mechanisms which will enable those students to complete the course and even to fulfil the curriculum for their degree",

author = "Anat Cohen",

year = "2015",

language = "English",

isbn = "978-615-5511-04-2",

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Web-Based Analysis of Student Activity for Predicting Dropout. / Cohen, Anat.
Proceedings of the European Distance and E-Learning Network 2015 Annual Conference: Expanding Learning Scenarios Opening Out the Educational Landscape. Barcelona, 2015. (European Distance and E-Learning Network).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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M3 - Conference contribution

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CY - Barcelona

ER -

Web-Based Analysis of Student Activity for Predicting Dropout

Abstract

Publication series

UN SDGs

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