Speaker: Prof. Ryan Baker, University of Pennsylvania, USA
Moderator: Prof. Michelle Banawan, Arizona State University, USA
Curated by Special Interest Group on AI-ED/ITS
Date: 10 September 2021
Time: 01:00-02:00 (GMT+0)
FREE Registration (due 8 September):https://apsce.net/webinar
Abstract:
Research in learning analytics and educational data mining has sometimes failed to distinguish between wheel-spinning and more productive forms of persistence, treating any student who completes more than ten items on a topic without mastering it as being in need of intervention. By contrast, the broader fields of education and human development have recognized the value of grit and persistence for long-term outcomes. In this talk, I discuss the longitudinal implications of wheel-spinning and productive persistence (completing many items but eventually mastering the topic) in online learning, utilizing a publicly available data set. This work connects behavior during learning in middle school mathematics to a student’s eventual enrollment (or failure to enroll) in college. We find that productive persistence during middle school mathematics is associated with a higher probability of college enrollment, whereas wheel-spinning during middle school mathematics is not statistically significantly associated with college enrollment. The findings around productive persistence remain statistically significant even when controlling for affect and disengaged behavior
