Quality (philosophy)

World Wide Web

Mathematics education

Philosophy

Construct (python library)

The Internet

Computer science

Dropout (neural networks)

Sign (mathematics)

Psychology

Mathematics

Web modeling

Learning analytics

Epistemology

Clickstream

Mathematical analysis

Data science

Artificial intelligence

Web API

Programming language

Perspective (graphical)

Machine learning

Computers & Education

Understanding the dropout phenomenon has advanced from viewing it as a sign of deficient quality to viewing it as an explicit sign of individual choice, which highlights the importance of investigating how dropouts learn in massive open online courses (MOOCs). Nevertheless, the short, limited and heterogeneous behaviours of individual dropouts create challenges for understanding how dropouts learn over time. Taking a systematic network perspective, this study used clickstream data to build a flow network model of collective attention to investigate how dropouts learn in XuetangX's Introduction to Psychology (2018 autumn). The results showed that the quantification of behavioural data presented a stereotypical image of dropouts, but the network analytics presented a rather different picture of how dropouts learn. Recognising the distinct roles of introductory learning resources could prevent dropping out and improve the accuracy of prediction models. Interestingly, the assessments embedded in the MOOCs performed a scaffolding role in guiding dropouts to learn. Thus, redesigning quizzes or examinations in open and flexible learning environments to construct a minimum cost network of collective attention is vital to making this online space cost effective for learners at risk.

The learning behaviours of dropouts in MOOCs: A collective attention network perspective