Sequential-Global Learning Style Detection Based on Users' Navigation Patterns in the Prerequisite Structure

摘要

The preferred way people apply in learning is known as learning style. Adapting different learning strategies to different learning styles yields a better learning outcome. In this paper, we describe a novel rule-based approach to detect the students' learning styles (sequential/global) by analyzing their navigation patterns in the prerequisite structure. In order to evaluate the accuracy of the proposed approach, a case study in dance education was conducted, 32 students were asked to learn 10 dances by browsing the prerequisite structure in a dance education system. Students' browsing histories are recorded and analyzed so that their learning styles are extracted. The result shows that our approach is optimistic.