zamani, maryam

The Effect of Teacher-Engaging Message On Student Motivation to Learn: A Sentiment Analysis

Gathering information about students’ reactions to open-ended questions is useful for understan‎ding the value of communication made by educators an‎d how it relates to motivation. The current research used sentiment analysis, which is an artificial intelligence technology, on students’ open-ended answers about Teacher-Engaging Messages. The proposed research used Natural Language Processing (NLP) polarity scores to investigate how Teacher-Engaging Messages have an effect on students’ sentiments. Finally, it investigated how students’ sentiments can be a mediator for Teacher-Engaging Messages on Motivation to Learn (MTL). A quantitative ex post facto research design was used with a total of 595 Iranian EFL (English as a Foreign Language) learners from Isfahan University of Technology as participants. Data were collected using the Teacher Engaging Message Scale (TEMS), the Academic Motivation Scale (AMS), an‎d an open-ended item. Data was analyzed using Structural Equation Modeling (SEM) to test the hypothesized links. Results indicated that students who perceive that their teachers use engaging messages have a more positive sentiment toward their teacher communication. Furthermore, results indicated that student sentiment acted as a partial mediator of the relationship between teachersʹ engaging messages an‎d student motivation to learn. Findings demonstrate the usefulness of sentiment analysis as a credible approach to examining the affective dimension of teacher-student interaction an‎d suggest the usefulness of collaboration between educational psychology an‎d artificial intelligence to obtain data-informed understan‎dings of the way teacher communication influences student motivation an‎d engagement in an EFL context. Keywords: Teacher-Engaging Messages; Sentiment Analysis; Motivation to learn; Natural Language processing; Teacher-student interaction; EFL context; Academic Motivation