Design and Implementation of a Smart Feedback System for Lecturers Using Sentiment
Abstract
Traditional feedback methods are often manual, time-consuming, and susceptible to subjective interpretation, which limits their effectiveness for timely instructional improvement. To address these challenges, a web-based system was developed to streamline feedback collection and management. The system integrates a Flask-based sentiment analysis engine powered by Natural Language Processing (NLP) techniques to automatically classify student feedback into positive, negative, or neutral categories. The proposed system includes a user-friendly student feedback interface, a secure lecturer dashboard, automated sentiment classification, and graphical visualization of feedback trends using charting tools. These features work together to provide an efficient and structured approach to feedback analysis. System testing confirmed accurate sentiment classification, seamless integration between components, and effective real-time processing of feedback data. The system demonstrates how artificial intelligence can enhance teaching evaluation by providing lecturers with timely, data-driven insights that support continuous improvement in instructional delivery.
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References
Alosaimi, W., Saleh, H., Hamzah, A. A., El-Rashidy, N., Alharb, A., Elaraby, A., & Mostafa, S. (2024). ArabBert-LSTM: Improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory. Frontiers in Artificial Intelligence, 7, 1408845. https://doi.org/10.3389/frai.2024.1408845
Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2013). SA-E: Sentiment analysis for education. Proceedings of the 13th Koli Calling International Conference on Computing Education Research, 126-138.
Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315-1325. https://doi.org/10.1080/02602938.2018.1463354
Ellis, R., & Herrington, A. (2024). Exploring the use of artificial intelligence in the delivery of effective feedback. Assessment & Evaluation in Higher Education, 49(8), 1156-1170. https://doi.org/10.1080/02602938.2024.2415649
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112. https://doi.org/10.3102/003465430298487
Kastrati, Z., Dalipi, F., Imran, A. S., Pireva Nuci, K., & Wani, M. A. (2021). Sentiment analysis of students' feedback with NLP and deep learning: A systematic mapping study. Applied Sciences, 11(9), 3986. https://doi.org/10.3390/app11093986
Tripty, N. I., Islam, S., & Rahman, M. A. (2024). BERT applications in natural language processing: a review. Artificial Intelligence Review, 58(3), 1-45. https://doi.org/10.1007/s10462-025-11162-5
Van der Schaaf, M., Donkers, J., Slof, B., Molenaar, I., Segers, M., & Kester, L. (2024). A systematic review of the key components of online peer feedback practices in higher education. Higher Education Research & Development, 43(8), 2135-2152. https://doi.org/10.1080/07294360.2023.2284817
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008.
Rodriguez, C., & Kim, H. (2025). Implementing artificial intelligence in academic and administrative processes through responsible strategic leadership in higher education institutions. Frontiers in Education, 10, 1548104. https://doi.org/10.3389/feduc.2025.1548104
Rodriguez, M., Thompson, K., & Lee, J. (2025). Generalizing sentiment analysis: A review of progress, challenges, and emerging directions. Social Network Analysis and Mining, 15(1), 67. https://doi.org/10.1007/s13278-025-01461-8
Winstone, N. E., & Nash, R. A. (2016). The developing engagement with feedback toolkit (DEFT). https://www.heacademy.ac.uk/resource/developing-engagement-feedback-toolkit-deft
Yin, K., Liu, J., & Chen, S. (2024). Advanced BERT models for sentiment analysis: Performance evaluation and comparison. Natural Language Engineering, 30(2), 234-251. https://doi.org/10.1017/S1351324923000456
Zhang, H., Shafiq, M. O., & Cassee, N. (2024). Survey of transformers and towards ensemble learning using transformers for natural language processing. Journal of Big Data, 11, 25.
https://doi.org/10.1186/s40537-023-00842-0
Jahin, M. A., Shovon, M. S. H., Mridha, M. F., & Islam, M. R. (2024). A hybrid transformer and attention-based recurrent neural network for robust and interpretable sentiment analysis of tweets. Scientific Reports, 14, 24882.
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