Online persistence in higher education web-supported courses

This research consists of an empirical study of online persistence in Web-supported courses in higher education, using Data Mining techniques. Log files of 58 Moodle websites accompanying Tel Aviv University courses were drawn, recording the activity of 1189 students in 1897 course enrollments durin...

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Bibliographic Details
Published in:The Internet and higher education 2011-03, Vol.14 (2), p.98-106
Main Authors: Hershkovitz, Arnon, Nachmias, Rafi
Format: Article
Language:English
Subjects:
Algorithms
College Instruction
Courses
Data Processing
Educational data mining
Enrollment
Foreign Countries
Higher Education
Internet
Israel (Tel Aviv)
Learning management systems
Online activity
Pattern Recognition
Persistence
Recording
Web Based Instruction
Web Sites
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Summary:This research consists of an empirical study of online persistence in Web-supported courses in higher education, using Data Mining techniques. Log files of 58 Moodle websites accompanying Tel Aviv University courses were drawn, recording the activity of 1189 students in 1897 course enrollments during the academic year 2008/9, and were analyzed with statistical procedures and the Decision Tree algorithm. This yielded five groups of students whose behavior throughout the semester was described: Low-extent Users, Late Users, Online Quitters, Accelerating Users, and Decelerating Users. Results suggest that 46% of the students either decelerated their online activity or totally quit on the other hand, 42% either accelerated their activity or utilized the course website only towards the end of the semester. Additional state-or-trait analysis showed that type of persistence of online activity might be explained by both personal and course characteristics.
ISSN:1096-7516
1873-5525
DOI:10.1016/j.iheduc.2010.08.001