Modeling collective attention in online and flexible learning environments

Understanding how collective attention flow circulates amid an over-abundance of knowledge is a key to designing new and better forms of online and flexible learning experiences. This study adopted an open flow network model and the associated distance metrics to gain an understanding of collective attention flow using clickstream data in a massive open online course. Various patterns and dynamics of attention flow were identified and are discussed here in relation to learning performance. The results show that the effective accumulation, circulation, and dissipation of attention flow are important contributors to academic attainment. Understanding the patterns and dynamics of attention flow will allow us to design cost-effective learning resources to prevent learners from becoming overloaded.