Dropout management in online learning systems

ABSTRACT

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.

KEYWORDS:

• Online learning
• knowledge
• YouTube
• comments
• dropout
• education

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://ocw.mit.edu/about/milestones/

2 We thank one of the anonymous reviewers for this suggestion.

3 Initial diagnostics during data analysis indicated an almost perfect (and negative) correlation between count of likes and dislikes. Therefore, of the two indicators of quantitative measure, we removed the dislike indicator to avoid the problem of singularity during PLS estimation.

4 Variance in eWoM is the variation in the number of ratings about the affective content of comments or, the degree of heterogeneity among customers’ evaluations (Kostyra et al. 2016).