An empirical study of using sequential behavior pattern mining approach to predict learning styles

摘要

The authors of this paper set out to determine if a group of learners' sequential behavior patterns could be used to classify their learning style. Their approach recorded the interactions of learners in an e-learning environment, and then used sequential pattern mining techniques to separate learners into different learning styles. Whilst there are many instruments that categorize learning style, the authors chose the Myers-Briggs Type Indicator as the learning style model. The conclusions from this research indicate that the authors' approach separated learners into different learning styles within each dimension of the Myers-Briggs Type Indicator with a high level of accuracy. There is considerable evidence from classroom research that matching learner style to course delivery yields the greatest success, and the authors' research brings this concept to e-learning environments.