User navigational behavior in e-learning virtual environments

In this paper, we describe the navigational behavior of the students of an 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 is 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 lifelong 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.

[1]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[2]  Peter Brusilovsky,et al.  Methods and techniques of adaptive hypermedia , 1996, User Modeling and User-Adapted Interaction.

[3]  Roger Nkambou,et al.  Hierarchical Representation and Evaluation of the Student in an Intelligent Tutoring System , 2002, Intelligent Tutoring Systems.

[4]  Alexandros Paramythis,et al.  Adaptive Learning Environments and e-Learning Standards. , 2004 .

[5]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[6]  Judy Kay,et al.  Creating User Models from Web Logs , 2003 .

[7]  Doug Riecken,et al.  Introduction: personalized views of personalization , 2000, CACM.

[8]  Marti A. Hearst,et al.  The state of the art in automating usability evaluation of user interfaces , 2001, CSUR.

[9]  Albert Sangrà.  A New Learning Model for the Information and Knowledge Society: The Case of the Universitat Oberta de Catalunya (UOC), Spain , 2002 .

[10]  R Barthel,et al.  Standardization in e-Learning. The "Sharable Content Object Reference Model (SCORM)” , 2004 .

[11]  Gregor Kennedy,et al.  GENERIC USAGE MONITORING OF PROGRAMMING STUDENTS , 2003 .

[12]  Colin Tattersall,et al.  Swarm-Based Adaptation: Wayfinding Support for Lifelong Learners , 2004, AH.

[13]  Enric Mor,et al.  Analysis Of User Navigational Behavior ForE-learning Personalization , 2006 .

[14]  Khaled M. Hammouda,et al.  Data Mining in E-Learning , 2007 .

[15]  Enric Mor,et al.  E-learning personalization based on itineraries and long-term navigational behavior , 2004, WWW Alt. '04.

[16]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[17]  Hendrik Blockeel,et al.  Web mining research: a survey , 2000, SKDD.

[18]  Sebastián Ventura,et al.  Data Mining in E-learning , 2006 .

[19]  Peter Brusilovsky,et al.  Adaptive Hypermedia , 2001, User Modeling and User-Adapted Interaction.

[20]  Andrzej Skowron,et al.  Proceedings of the 2005 IEEE / WIC / ACM International Conference on Web Intelligence , 2005 .

[21]  Albert Sangragrave.  A New Learning Model for the Information and Knowledge Society: The Case of the UOC , 2002 .