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

Autor: Topali, Paraskevi, Chounta, Irene Angelica, Martínez Monés, Alejandra, Dimitriadis Damoulis, Ioannis
Jazyk: angličtina
Rok vydání: 2023
Předmět:
ISSN: 2020-1125
Popis: Producción Científica
Background:Providing feedback in massive open online courses (MOOCs) is chal-lenging due to the massiveness and heterogeneity of learners' population. Learninganalytics (LA) solutions aim at scaling up feedback interventions and supportinginstructors in this endeavour.Paper Objectives:This paper focuses on instructor-led feedback mediated by LAtools in MOOCs. Our goal is to answer how, to what extent data-driven feedback isprovided to learners, and what its impact is.Methods:We conducted a systematic literature review on the state-of-the-art LA-informed instructor-led feedback in MOOCs. From a pool of 227 publications, weselected 38 articles that address the topic of LA-informed feedback in MOOCs medi-ated by instructors. We applied etic content analysis to the collected data.Results and Conclusions:The results revealed a lack of empirical studies exploring LA todeliver feedback, and limited attention on pedagogy to inform feedback practices. Our find-ings suggest the need for systematization and evaluation of feedback. Additionally, there isa need for conceptual tools to guide instructors' in the design of LA-based feedback.Takeaways:We point out the need for systematization and evaluation of feedback. Weenvision that this research can support the design of LA-based feedback, thus contribut-ing to bridge the gap between pedagogy and data-driven practice in MOOCs.
Consejo de Investigación de Estonia (PSG286)
Ministerio de Ciencia e Innovación - Fondo Europeo de Desarrollo Regional y la Agencia Nacional de Investigación (grant PID2020-112584RB-C32) and (grant TIN2017-85179-C3-2-R)
Junta de Castilla y León - Fondo Social Europeo y el Consejo Regional de Educación (grant E-47-2018-0108488)
Databáze: OpenAIRE