Analzing attention streams to enhance knowledge transmission on MOOC-based learning platforms: A Case Study of XuetangX's Photojournalism Course

The popularity of MOOC-based learning platforms has led to a surge in the number of students enrolling in online courses. However, the effectiveness of knowledge transmission on these platforms varies greatly due to the heterogeneity of online learning behavior. To help teachers and learning platforms design better courses, analyzing the knowledge transmission of online learning is crucial. This study adopts a "stream" perspective to visualize the concept of knowledge, and uses clickstream data to build an open stream network of students' collective attention streams. By studying attention streams, knowledge streams were simulated and quantified. The study focused on XuetangX's boutique course, Photojournalism (Fall 2016), and classified students as dropouts and non-dropouts based on whether they could stay in the course for the whole duration. Comparative learning analysis and social network analysis techniques were used to study the three processes of attention flow. The results showed that courseware resources and course announcement information played a crucial role in guiding the direction of students' attention flow and acted as gateways for attention accumulation. MOOC-embedded tests played a scaffolding role in the flow process, strengthening the supervision of online students' learning and helping them receive knowledge better. This also promoted the flow of knowledge in the network and reduced dissipation. In conclusion, this study highlights the importance of analyzing attention streams to understand the effectiveness of knowledge transmission on MOOC-based learning platforms. The findings suggest that optimizing courseware resources, course announcement information, and MOOC-embedded tests can help to enhance the overall learning experience of online students.