Tree Structure of Collective Attention Network: Revisiting the Problem of Dropout - LAK 2020 скачать в хорошем качестве

Tree Structure of Collective Attention Network: Revisiting the Problem of Dropout - LAK 2020

: Treating the dropout phenomenon as a sign of an individual’s choice highlights the importance of understanding how dropouts learn in MOOCs. Conventional learning analytics methods failed to make sense of limited behavior data left by dropouts. This study uses the minimum spanning tree of collective attention network to investigate how dropouts behave in a selected MOOC. It is interesting to note that assessments embedded in the MOOCs seem to play a rather important role in guiding dropouts to learn. Redefining assessment in open and flexible learning environments to construct a minimum cost network of collective attention is vital to make this online space cost-effective for better learning.