Abstract
AbstractGathering information from students’ answers to open-ended questions helps to assess the quality of teachers’ practices and its relations with students’ motivation. The present study aimed to use sentiment analysis, an artificial intelligence-based tool, to examine students’ responses to open-ended questions about their teacher’s communication. Using the obtained sentiment scores, we studied the effect of teachers engaging messages on students’ sentiment. Subsequently, we analysed the mediating role of this sentiment on the relation between teachers’ messages and students’ motivation to learn. Results showed that the higher the students’ perceived use of engaging messages, the more positive their sentiments towards their teacher’s communication. This is an important issue for future research as it shows the usefulness of sentiment analysis for studying teachers’ verbal behaviours. Findings also showed that sentiment partially mediates the effect of teachers engaging messages on students’ motivation to learn. This research paves the way for using sentiment analysis to better study the relations of teachers’ behaviours, students’ sentiments and opinions, and their outcomes.
Funder
Ministerio de Ciencia, Innovación y Universidades
Las Palmas University Foundation
Universidad de las Palmas de Gran Canaria
Publisher
Springer Science and Business Media LLC
Reference110 articles.
1. Adediwura, A. A., & Tayo, B. (2007). Perception of teachers’ knowledge, attitude and teaching skills as predictor of academic performance in nigerian secondary schools. Educational Research and Review, 2(7), 165–171. http://www.academicjournals.org/ERR.
2. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705.
3. Alhija, F. N. A., & Fresko, B. (2009). Student evaluation of instruction: What can be learned from students’ written comments? Studies in Educational Evaluation, 35(1), 37–44. https://doi.org/10.1016/j.stueduc.2009.01.002.
4. Álvarez-Álvarez, C., Sanchez-Ruiz, L., Ruthven, A., & Montoya, J. (2019). Innovating in university teaching through classroom interaction. Journal of Education Innovation and Communication, 1(1), 8–18. https://doi.org/10.34097/jeicom_1_1_1.
5. Andersson, E., Dryden, C., & Variawa, C. (2018). Applying machine learning to student feedback through sentiment analysis. 2018Canadian Engineering Education Association (CEEA-ACEG18) Conference, 2–7. https://doi.org/10.24908/pceea.v0i0.13059
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