150 Psychologie
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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.
Kopainsky et al., (2020) examines intended and unintended transition effects of the Swiss food system on the system's structure and the environment. Kopainsky et al.'s research refers to studies on and is embedded in research streams in global health (Jamison et al., 2013) and sustainable food systems (Willett et al., 2019). It also addresses many of Steffen et al.'s (2015) planetary boundaries, the United Nations' (2015) sustainability goals (SDGs), and potentially could address how they are interrelated, following Randers et al. (2019). It is furthermore embedded in research on natural and human systems, particularly in the intertwined business, supply and demand, governance, ecological and health feedback loops (Swinburn et al., 2019). This feedback view enhances understanding and assessment of drivers towards improving human and ecological health and mitigating climate change.