Turning-Point Detection in Affiliation and Affect within Longitudinal MOOC Dialogues
DOI:
https://doi.org/10.66238/ijrss.v6i2.131Keywords:
Affective Computing, Social Affiliation, Longitudinal Analysis, Turning-Point DetectionAbstract
The rapid proliferation of Massive Open Online Courses has significantly expanded access to global education, yet the environments often suffer from low retention and engagement rates. While cognitive factors contributing to student success are well documented, the socio-emotional dimensions of learning, specifically affiliation and affect, remain insufficiently explored in longitudinal contexts. This paper investigates the dynamic fluctuations of peer affiliation and emotional affect within collaborative discourse over extended educational periods. By employing advanced natural language processing and time-series segmentation techniques, this study identifies critical turning points in student interactions. Turning points are defined as significant, sustained shifts in the trajectory of a learner's emotional valence or social connectedness. The research utilizes a large-scale dataset of forum dialogues from a sixteen-week data science course, applying change-point detection algorithms to continuous streams of textual data. The findings reveal that turning points in affect frequently precede shifts in affiliation, suggesting that emotional transitions serve as early indicators of social integration or withdrawal. Furthermore, negative turning points are strongly correlated with subsequent disengagement and dropout. The insights provided by this study offer a robust foundation for developing adaptive pedagogical interventions that can provide timely support to learners experiencing socio-emotional distress, thereby enhancing overall retention and learning outcomes in digital environments.
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