TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Dash, Rupanwita
A1  - Ranjan, Kumar Rakesh
A1  - Rossmann, Alexander
T1  - Dropout management in online learning systems
JF  - Behaviour & information technology
N2  - We examine the role of communication from users on dropout from digital learning systems to answer the following questions: (1) how does the sentiment within qualitative signals (user comments) affect dropout rates? (2) does the variance in the proportion of positive and negative sentiments affect dropout rates? (3) how do quantitative signals (e.g. likes) moderate the effect of the qualitative signals? and (4) how does the effect of qualitative signals on dropout rates change across early and late stages of learning? Our hypotheses draws from learning theory and self-regulation theory, and were tested using data of 447 learning videos across 32 series of online tutorials, spanning 12 different fields of learning. The findings indicate a main effect of negative sentiment on dropout rates but no effect of positive sentiment on preventing dropout behaviour. This main effect is stronger in the early stages of learning and weakens at later stages. We also observe an effect of the extent of variance of positive and negative sentiments on dropout behaviour. The effects are negatively moderated by quantitative signals. Overall, making commenting more broad-based rather than polarised can be a useful strategy in managing learning, transferring knowledge, and building consensus.
KW  - online learning
KW  - knowledge
KW  - YouTube
KW  - comments
KW  - dropout
KW  - education
Y1  - 2022
SN  - 0144-929x
SS  - 0144-929x
U6  - https://doi.org/10.1080/0144929X.2021.1910730
DO  - https://doi.org/10.1080/0144929X.2021.1910730
VL  - 41
IS  - 9
SP  - 1973
EP  - 1987
S1  - 15
PB  - Taylor & Francis
CY  - London
ER  -