Title: How do teachers' emotions affect student sentiment in MOOCs? A study using facial emotion recognition and sentiment analysis

Authors: Vishnu Lal; Avuduri Venkata Amarnadh Reddy; G. Varaprasad

Addresses: Symbiosis Institute of Business Management, Bengaluru Campus, Symbiosis International (Deemed University), Pune, India ' Department of Mechanical Engineering, National Institute of Technology Calicut, Kattangal, Kerala 673601, India ' Department of Mechanical Engineering, National Institute of Technology Calicut, Kattangal, Kerala 673601, India

Abstract: This study investigates the impact of teachers' emotions on student sentiment in MOOCs and exploring the potential relationship between emotional expressions conveyed by instructors and the emotional experiences of learners. The study uses the bidirectional encoder representations from transformers (BERT) model for sentiment analysis and the convolutional neural network (CNN) model for facial emotion recognition to analyse the emotions of teachers using instructional videos, discussion forums, and written communication. The findings demonstrate a significant correlation between teachers' emotions and student sentiment in MOOCs. Instructors displaying positive emotions elicited higher levels of student engagement and satisfaction. Conversely, negative emotional expressions were associated with decreased student sentiment and reduced motivation. The study makes a significant contribution to the emerging area of emotional dynamics in e-learning by providing vital insights for developing instructional strategies, course design and development of emotionally responsive educational technologies.

Keywords: deep learning; emotional intelligence; sentiment analysis; facial emotion recognition; FER; convolutional neural network; CNN.

DOI: 10.1504/IJLT.2025.149282

International Journal of Learning Technology, 2025 Vol.20 No.3, pp.344 - 364

Received: 04 Oct 2023
Accepted: 04 Dec 2023
Published online: 22 Oct 2025 *

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