Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 98-106 |
| Number of pages | 9 |
| Journal | Internet and Higher Education |
| Volume | 14 |
| Issue number | 2 |
| DOIs | |
| State | Published - Mar 2011 |
Keywords
- Educational data mining
- Learning management systems
- Online activity
- Persistence
All Science Journal Classification (ASJC) codes
- Education
- Computer Science Applications
- Computer Networks and Communications