Conflicted about online learning?: Using sentiment analysis to explore learner approach-avoidance motivation

Abstract

PurposeStudent motivation underpins the challenge of learning, made more complex by the move to online education. While emotions are integral to students' motivation, research has, to date, overlooked the dualistic nature of emotions that can cause stress. Using approach-avoidance conflict theory, the authors explore this issue in the context of novel online students' responses to a fully online class.Design/methodology/approachUsing a combination of critical incident technique and laddering, the authors implemented the big data method of sentiment analysis (SA) which results in approach tables with 1,318 tokens and avoid tables with 1,090 tokens. Using lexicon-based SA, the authors identify tokens relating to approach, avoid and mixed emotions.FindingsThe authors implemented the big data method of SA which results in approach tables with 1,318 tokens and avoid tables with 1,090 tokens. Using lexicon-based SA, the authors identify tokens relating to approach, avoid and mixed emotions. These ambivalent emotions provide an opportunity for teachers to rapidly diagnose and address issues of student engagement in an online learning class.Originality/valueResults demonstrate the practical application of SA to unpack the role of emotions in online learner motivation.