Affiliations: [a] Computational Sciences Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Tonantzintla, Puebla, México
| [b] Department of Computer Systems Engineering, Universidad de la Sierra, Moctezuma, Sonora, México
| [c] Department of Postgraduate Studies and Research, Instituto Tecnológico de Nogales, Sonora, México
Correspondence:
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Corresponding author. Jesús Miguel Garcıa-Gorrostieta, Department of Computer Systems Engineering, Universidad de la Sierra, Moctezuma, Sonora, México. E-mail: [email protected].
Abstract: Emotion detection in educational dialogues, particularly within student-teacher interactions, has become a crucial research area for improving the learning experience. In this paper, we employ two models, one generic Bidirectional Encoder Representations from Transformers (BERT) and the Emotion detection model Robustly Optimized BERT Approach (EmoRoBERTa), to automatically classify emotions in a corpus of student-teacher chat interactions. Then subsequently, we validate these classifications using a scheme based on oracles, employing two generative large language models (ChatGPT and Bard). Experiments on emotion detection in dialogues between students and teachers revealed that EmoRoBERTa exhibited a reasonable level of agreement with the oracles, while ChatGPT demonstrated the highest consistency with EmoRoBERTa’s predictions. Furthermore, we identified the impact of specific words on emotion classification, offering insights into the decision-making process of these models. The results not only highlight the prominent presence of emotions like approval, gratitude, curiosity, disapproval, amusement, confusion, remorse, joy, and surprise but also provide substantial support for the utilization of the proposed emotion detection model to enhance the student learning environment. Exploring the emotional aspects of educational dialogues holds the potential to enhance instruction methods, provide timely assistance to students in need, and create an improved learning atmosphere.
Keywords: Emotion detection, learning interaction, transfer learning, large language models, active learning
