C4.5: programs for machine learning
C4.5: programs for machine learning
Cognitive styles and virtual environments
Journal of the American Society for Information Science - Special topic issue: individual differences in virtual environments
Decision tree classification of spatial data streams using Peano Count Trees
Proceedings of the 2002 ACM symposium on Applied computing
Data Mining with C4.5 and Interactive Cartographic Visualization
UIDIS '99 Proceedings of the 1999 User Interfaces to Data Intensive Systems
Learning style, learning patterns, and learning performance in a WebCT-based MIS course
Information and Management
DrC4.5: Improving C4.5 by means of prior knowledge
Proceedings of the 2005 ACM symposium on Applied computing
What affect student cognitive style in the development of hypermedia learning system?
Computers & Education
A scalable decision tree system and its application in pattern recognition and intrusion detection
Decision Support Systems
The influence of system characteristics on e-learning use
Computers & Education
Engineering Applications of Artificial Intelligence
Computational Statistics & Data Analysis
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Artificial Intelligence Review
Investigating attributes affecting the performance of WBI users
Computers & Education
Demographic differences in how students navigate through MOOCs
Proceedings of the first ACM conference on Learning @ scale conference
Hi-index | 12.05 |
There has been a proliferation of web-based learning programs (WBLPs). Unlike traditional computer-based learning programs, WBLPs are used by a population of learners who have diverse background. How different learners access the WBLPs has been investigated by several studies, which indicate that cognitive style is an important factor that influences learners' preferences. However, these studies mainly use statistical methods to analyze learners' preferences. In this paper, we propose to analyze learners' preferences with a data mining technique. Findings in our study show that Field Independent learners frequently use backward/forward buttons and spent less time for navigation. On the other hand, Field Dependent learners often use main menu and have more repeated visiting. Implications for these findings are discussed.