Open AccessVideo Features, Engagement, and Patterns of Collective Attention Allocation
Author(s) -
Jingjing Zhang,
YiCheng Huang,
Ming Gao
Publication year - 2022
Publication title -
journal of learning analytics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.084
H-Index - 7
ISSN - 1929-7750
DOI - 10.18608/jla.2022.7421
Subject(s) - learning analytics , computer science , perspective (graphical) , analytics , process (computing) , visual analytics , user engagement , collaborative learning , educational technology , multimedia , data science , human–computer interaction , world wide web , psychology , visualization , mathematics education , knowledge management , artificial intelligence , operating system
Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible learning environments. This study selected “Fundamentals of C++ programming (Spring 2019)” on XuetangX as an example through which to observe the allocation patterns of attention within MOOC videos, as well as how video features and engagement correlate with the accumulation, circulation, and dissipation pattern of collective attention. The results showed that the types of instructions in videos predicted attention allocation patterns, but they did not predict the engagement of video watching. Instead, the length and whether the full screen was used in the videos had a strong impact on engagement. Learners were more likely to reach a high level of engagement in video watching when their attention had been circulated around the videos. The results imply that understanding the patterns and dynamics of attention flow and how learners engage with videos will allow us to design cost-effective learning resources to prevent learners from becoming overloaded.