“ Turn on , Tune in , Drop out ” : Anticipating student dropouts in Massive Open Online Courses

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How researchers see this paper

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Dropout rates of massive open online courses : behavioural patterns (2014, background): “A basic comparison was completed to show the differences in participation rates of the two modes.”
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (2019, background): “For instance, predicting when a student is likely to drop out of a MOOC course is important to develop early intervention measures [23, 46] and predicting when a user is likely to turn malicious on social platforms, like Reddit and Wikipedia, ensures platform integrity [9, 25, 26].”
Temporal Models for Predicting Student Dropout in Massive Open Online Courses (2015, methodology): “Feature [7,34,9,2,0,7,5] Zeros [6,3,12,4,1,8,3] Zeros Zeros”
Equity Beyond Bias in Language Technologies for Education (2019, background): “In text-only settings online, sentiment has been a key part of prediction of attrition rates in MOOCs (Yang et al., 2013; Wen et al., 2014), especially when combined with micro-level instantaneous data like clickstream events (Crossley et al., 2016).”