Home⇛Journal of Interdisciplinary Perspectives⇛vol. 3 no. 8 (2025)

AI-Driven Insights from Student Feedback for Teacher Improvement

Marie Grace V. Ortiz | Menchita F. Dumlao

Discipline: Education

 

Abstract:

This study used Natural Language Processing (NLP) and Artificial Intelligence (AI) to analyze ten years of teacher evaluations. The research leveraged VADER and NRC for sentiment analysis and LDA for topic modeling to extract key themes. The Google Gemini AI model then generated actionable recommendations for pedagogical improvement. Analysis of 9,052 textual comments revealed a predominantly positive (71%) to neutral (27%) comments, and LDA identified eight distinct topics. The AIdriven analysis successfully provided targeted suggestions for pedagogical enhancement, offering a pathway toward data-informed professional growth for educators. However, multilingual feedback presented challenges for comprehensive analysis.



References:

  1. Abry, T., Granger, K., Bryce, C., Taylor, M., Swanson, J., & Bradley, R. (2018). First grade classroom-level adversity: associations with teaching practices, academic skills, and executive functioning. School Psychology Quarterly, 33(4), 547–560. https://doi.org/10.1037/spq0000235 
  2. Al-Garadi, M. A. ., Yang, Y., Guo, Y., Kim, S., Love, J. S., Perrone, J., & Sarker, A. (2021). Large-scale social media language analysis reveals emotions and behaviours associated with nonmedical prescription drug use. Health Data Science. https://doi.org/10.34133/2022/9851989 
  3. Balam, E. M., & Shannon, D. M. (2010). Student ratings of college teaching: A comparison of faculty and their students. Assessment and Evaluation in Higher Education, 35(2), 209–221. https://doi.org/10.1080/02602930902795901 
  4. Bill & Melidan Gates Foundation. (2012). Asking students about teaching. Bill & Melinda Gates Foundation, 1–28. https://eric.ed.gov/?id=ED566384 
  5. Boonen, A. J. H., Reed, H. C., Schoonenboom, J., & Jolles, J. (2016). It’s not a math lesson-we’re learning to draw! teachers’ use of visual representations in instructing word problem solving in sixth grade of elementary school. Frontline Learning Research, 4(5), 34–61. https://doi.org/10.14786/flr.v4i5.245 
  6. Ching, G. (2018). A literature review on the student evaluation of teaching. Higher Education Evaluation and Development, 12(2), 63–84. https://doi.org/10.1108/heed-04-2018-0009 
  7. Ed, E. I. (2000). ERIC t , Oil Digests. 1981, 1–6.
  8. Esparza, G. G., de-Luna, A., Zezzatti, A. O., Hernandez, A., Ponce, J., Álvarez, M., Cossio, E., & Nava, J. de J. (2018). A sentiment analysis model to analyze students reviews of teacher performance using support vector machines. Advances in Intelligent Systems and Computing, 620, 157–164. https://doi.org/10.1007/978-3-319-62410-5_19 
  9. Hashim, S., Omar, M. K., Jalil, H. A., & Sharef, N. M. (2022). Trends on technologies and artificial intelligence in education for personalized learning: systematic literature review. International Journal of Academic Research in Progressive Education and Development, 11(1). https://doi.org/10.6007/IJARPED/v11-i1/12230 
  10. Heaton, D., Clos, J., Nichele, E., & Fischer, J. (2023). Critical reflections on three popular computational linguistic approaches to examine twitter discourses. Peerj Computer Science. https://doi.org/10.7717/peerj-cs.1211 
  11. Heimerl, F., Lohmann, S., Lange, S., & Ertl, T. (2014). Word cloud explorer: Text analytics based on word clouds. Proceedings of the Annual Hawaii International Conference on System Sciences, 1833–1842. https://doi.org/10.1109/HICSS.2014.231 
  12. Hutchinson, M., Coutts, R., Massey, D., Nasrawi, D., Fielden, J., Lee, M., & Lakeman, R. (2023). Student evaluation of teaching: reactions of Australian academics to anonymous non-constructive student commentary. Assessment and Evaluation in Higher Education. https://doi.org/10.1080/02602938.2023.2195598 
  13. Hutto, C., & Gilbert, E. (2014). Vader: a parsimonious rule-based model for sentiment analysis of social media text. International Aaai Conference on Web and Social Media, 216–225.
  14. Kreitzer, R. J., & Sweet-Cushman, J. (2022). Evaluating Student Evaluations of Teaching: a Review of Measurement and Equity Bias in SETs and Recommendations for Ethical Reform. Journal of Academic Ethics, 20(1), 73–84. https://doi.org/10.1007/s10805-021-09400-w 
  15. Leguey, S., Cid-Cid, A. I., Guede-Cid, R., & Prieto, J. (2023). An Exploratory Analysis of Major Dropdowns in Student Evaluation of Teaching Ratings in Higher Education. Multidisciplinary Journal of Educational Research, 13(1), 91–113. https://doi.org/10.17583/remie.10419
  16. Luan, H., Lai, H., Gobert, J. D., Yang, S. J., & Ogata, H. (2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.580820 
  17. Mahmood, A., Kamaruddin, S., Naser, R., & Nadzir, M. (2020). A combination of lexicon and machine learning approaches for sentiment analysis on facebook. Journal of System and Management Sciences., 10(3), 140–150. https://doi.org/10.33168/JSMS.2020.0310 
  18. Mujahid, M., Lee, E., Rustam, F., Washington, P. B., Ullah, S., Reshi, A. A., & Ashraf, I. (2021). Sentiment analysis and topic modeling on tweets about online education during covid-19. Applied Sciences (Switzerland), 11(18). https://doi.org/10.3390/app11188438 
  19. Nasim, Z., Rajput, Q., & Haider, S. (2017). Sentiment analysis of student feedback using machine learning and lexicon-based approaches. International Conference on Research and Innovation in Information Systems, ICRIIS. https://doi.org/10.1109/ICRIIS.2017.8002475 
  20. Newman, H., & Joyner, D. (2018). Sentiment analysis of student evaluations of teaching. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10948 LNAI, 246–250. https://doi.org/10.1007/978-3-319-93846-2_45 
  21. Ohtani, S. (2020). Exploring public interest from twitter in 2021 using natural language processing for post-2020 biodiversity conservation strategies. https://doi.org/10.21203/rs.3.rs-1468450/v1 
  22. Philip, R. (2020). Word cloud analysis and single word summarisation as a new paediatric educational tool: results of a neonatal application. Journal of Paediatrics and Child Health, 56(6), 873–877. https://doi.org/10.1111/jpc.14760 
  23. Rajput, Q., Haider, S., & Ghani, S. (2016). Lexicon-Based Sentiment Analysis of Teachers’ Evaluation. Applied Computational Intelligence and Soft Computing, 2016, 1–12. https://doi.org/10.1155/2016/2385429 
  24. Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1), 54. https://doi.org/10.1186/s41239-021-00292-9 
  25. Simmons, T. L. (1997). Student Evaluation of Teachers: Professional Practice or Punitive Policy? JALT Testing & Evaluation SIG Newsletter, 1(1), 12–19. https://teval.jalt.org/test/sim_1.htm 
  26. Spooren, P., Brockx, B., & Mortelmans, D. (2013). On the Validity of Student Evaluation of Teaching: The State of the Art. In Review of Educational Research (Vol. 83, Issue 4). https://doi.org/10.3102/0034654313496870 
  27. Sullivan, K. J., Burden, M., Keniston, A., Banda, J. M., & Hunter, L. (2020). Characterization of anonymous physician perspectives on covid-19 using social media data. Biocomputing 2021, 26, 95–106. https://pmc.ncbi.nlm.nih.gov/articles/PMC7958992/ 
  28. Tafazoli, D., Chirimbu, S., & Dejica-Carțiș, A. (2022). Web 2.0 in english language teaching: using word clouds. Professional Communication and Translation Studies, 167–172. https://doi.org/10.59168/XQDJ2288 
  29. Vrain, E., & Lovett, A. (2019). Using word clouds to present farmers’ perceptions of advisory services on pollution mitigation measures. Journal of Environmental Planning and Management, 63(6), 1132–1149. https://doi.org/10.1080/09640568.2019.1638232 
  30. Wang, J. (2025). The impact of AI teaching on teaching quality. International Journal of Web-Based Learning and Teaching Technologies, 20(1), 1–22. https://doi.org/10.4018/IJWLTT.376489 
  31. Wine, D. (2016). Using student feedback to enhance teacher evaluation. Teacher Education, Educational Leadership & Policy ETDs. https://digitalrepository.unm.edu/educ_teelp_etds/49
  32. Yuliansyah, H., Mulasari, S., Sulistyawati, S., & Ghozali, F. , S. B. (2024). Sentiment analysis of the waste problem based on youtube comments using vader and deep translator. Jurnal Media Informatika Budidarma, 8(1), 663. https://tinyurl.com/rkkwhefv 
  33. Zabaleta, F. (2007). The use and misuse of student evaluations of teaching. Teaching in Higher Education, 12(1), 55–76. https://doi.org/10.1080/13562510601102131 
  34. Zhang, J., & Zhang, Z. (2024). AI in teacher education: unlocking new dimensions in teaching support, inclusive learning, and digital literacy. Journal of Computer Assisted Learning, 40(4). https://doi.org/10.1111/jcal.12988 

Tools


Copyright © 2025  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic