Delving into instructor-led feedback interventions informed by learning analytics in massive open online courses

BackgroundProviding feedback in massive open online courses (MOOCs) is challenging due to the massiveness and heterogeneity of learners' population. Learning analytics (LA) solutions aim at scaling up feedback interventions and supporting instructors in this endeavour. Paper ObjectivesThis paper focuses on instructor-led feedback mediated by LA tools in MOOCs. Our goal is to answer how, to what extent data-driven feedback is provided to learners, and what its impact is. MethodsWe conducted a systematic literature review on the state-of-the-art LA-informed instructor-led feedback in MOOCs. From a pool of 227 publications, we selected 38 articles that address the topic of LA-informed feedback in MOOCs mediated by instructors. We applied etic content analysis to the collected data. Results and ConclusionsThe results revealed a lack of empirical studies exploring LA to deliver feedback, and limited attention on pedagogy to inform feedback practices. Our findings suggest the need for systematization and evaluation of feedback. Additionally, there is a need for conceptual tools to guide instructors' in the design of LA-based feedback. TakeawaysWe point out the need for systematization and evaluation of feedback. We envision that this research can support the design of LA-based feedback, thus contributing to bridge the gap between pedagogy and data-driven practice in MOOCs.