Discovery of Association Rule of Learning Action Based on Bayesian Network
Teng Liu, Yueqin Zhang, Jian Chen, Huajie Shen · 2018
In this study, we propose an approach based on Bayesian network to find the correlation of learning actions in micro-learning. The correlations are then used to optimize learning path for target learners. In details, A learning action is defined as an operating unit of learning activity, and a learning activity is defined as an educational process in order to achieve a learning goal [1]. Furthermore, we regard a learning activity as a learning cycle, which consists of a series of learning actions. And then, we use Bayesian network to analyze the datasets of learning logs which are a sequence of learning actions of learners, and attempt to discover the association rules of learning action in micro-learning cycles. The results of this study will be used to recommend learning path. The experimental results of this study show that most learners have their own learning style on choosing learning actions, therefore, a suitable learning style is important to help learners improve their learning efficiency.