User Navigational Behavior in e-Learning Virtual Environments

• Published in: IEEE/WIC/ACM International Conference on web intelligence pp. 243 - 249
• Main Authors: Carbo, Joan Marc, Mor, Enric, Minguillon, Julia
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 19.09.2005; IEEE
• Series: ACM Conferences
• Subjects: Applied computing > Document management and text processing > Document preparation > Multi > mixed media creation; Applied computing > Education; Computer science; Content management; Electronic learning; Environmental management; Human-centered computing > Human computer interaction (HCI); Human-centered computing > Human computer interaction (HCI) > Interaction paradigms > Hypertext > hypermedia; Information systems > Information retrieval > Users and interactive retrieval > Personalization; Information systems > World Wide Web > Web searching and information discovery > Personalization; Internet; Navigation; Pattern analysis; Performance analysis; Virtual environment; Web mining
• ISBN: 076952415X; 9780769524153
• DOI: 10.1109/WI.2005.155

In this paper we describe the navigational behavior of the students of a e-learning virtual environment, in order to determine whether such navigational patterns are related to the academic performance achieved by the students or not, and which behaviors can be identified as more successful. As an example, a subset of students taking a degree in Computer Science in a completely virtual online university are selected as the matter of study. Three levels of analysis are described: a session level, where students perform a few actions in a single session logged to the virtual campus; a course level, where all single sessions are joined to form a course navigational pattern; and a life-long learning level, where students enroll in several subjects each academic semester. A simple experiment is outlined for the course level to demonstrate the possibilities of such analysis in a virtual e-learning environment. This experiment shows that the information collected in this level is useful for understanding user behavior and the relationship with his or her academic achievements, and that some intuitive ideas about the relevance of specific user actions or particularities can be also better explained.