Computers in Human Behavior
Interaction is a principle of high-quality course design in online learning. Previous research shows that inter- action in Massive Open Online Courses is crucial for learner retention and course completion. Using panel network data of 386 MOOC learners, this study explored the mechanisms that drive learner-learner interaction over time, specifically, the patterns and evolution of learner-learner interaction in a MOOC through a stochastic- actor-oriented model. The results contradicted previous evidence that learners reciprocate open communication (i.e., replies) in discussion forums and tend to interact with those to whom their direct connections reply. The extent to which learners interact with others similar to themselves (i.e., homophily) was not a statistically significant predictor of learner-learner interaction over time. Popularity, as measured by open communication, suggested preferential attachment in MOOC learners. High levels of affective communication received (i.e., likes) reduced open communication over time. Implications for practice are discussed, and future research that analyzes the quality of open communication over time is recommended.
Castellanos-Reyes, D. (2021). The dynamics of a MOOC’s learner-learner interaction over time: A longitudinal network analysis. Computers in Human Behavior, 123(2021), 106880. https://doi.org/10.1016/j.chb.2021.106880 [IF = 6.829]