TY - JOUR
T1 - Online persistence in higher education web-supported courses
AU - Hershkovitz, Arnon
AU - Nachmias, Rafi
PY - 2011/3
Y1 - 2011/3
N2 - 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.
AB - 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.
KW - Educational data mining
KW - Learning management systems
KW - Online activity
KW - Persistence
UR - http://www.scopus.com/inward/record.url?scp=79952038239&partnerID=8YFLogxK
U2 - 10.1016/j.iheduc.2010.08.001
DO - 10.1016/j.iheduc.2010.08.001
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AN - SCOPUS:79952038239
SN - 1096-7516
VL - 14
SP - 98
EP - 106
JO - Internet and Higher Education
JF - Internet and Higher Education
IS - 2
ER -