Leveraging Artificial Intelligence to Predict Young Learner Online Learning Engagement

Abstract

The recent surge of online language learning services in the past decade has benefitted second language learners. However, there is a lack of understanding of whether learners, especially young learners, are engaged in online learning, and how educators can enhance the engagement of the online learning experience. This study examines an artificial intelligence (AI)- powered automated system that uses voice and facial recognition to track both teacher and learner speech, facial expressions, and interactions in real-time in a one-to-one 25-minute online English class. Each learner completed a learner engagement survey within 72 hours of the online class. Results demonstrated that young learners were highly engaged during this one-to-one online learning setting (mean = 4.5, out of 5). Learners’ frontal face exposure (indicating their attentiveness during class) and English proficiency levels are significant and positive predictors of learner engagement. Teachers’ total length of speech and instructional time tended toward significance in predicting learner engagement. Educational implications are discussed.

Keywords: Artificial Intelligence (AI), learner engagement, online tutoring, data science applications in education, English as a Foreign Language (EFL).