Enhancing learner autonomy with DDL: A case study of learners perspective

This paper studies how data-driven learning (DDL) and independent work may be used to enhance learners' autonomy. The research aims at analyzing and determining the effects of this approach on learner autonomy and motivation. The author conducts a survey, which included a questionnaire and an activity based part to find out learners perspective on enhancing their autonomy with DDL material assistance. The survey was conducted at Moscow State University of Food Production, in May 2020 during a lockdown period. The research findings based on the with a group of undegraded students (n = 48, A2 - B1) suggest using DDL materials in teaching is more motivating as the learners feel more engaged. Thus, we argue that DDL together with traditional forms of individual work enhances learner motivation, and consequently lead to better results in their studies. © 2020 ACM.