Mining Improvement Points for University Teaching Feedback Based on BERT Embedding and Sentiment Convolutional Network
Main Article Content
Abstract
University teaching feedback contains contextual semantics, implicit sentiment, and fragmented improvement suggestions, which makes automatic extraction difficult when only keyword or shallow syntactic features are used. To improve semantic understanding and emotion-aware localization, this study proposes a teaching-feedback mining model combining BERT embedding, a multi-channel sentiment convolutional network, emotional attention, and BiLSTM-CRF sequence labeling. After text cleaning, sentence segmentation, stop-word removal, and BIO-based sentiment annotation, BERT is used to generate deep contextual representations. Multi-scale convolutional kernels then extract local sentiment patterns, and an emotional attention mechanism enhances features highly related to improvement-point identification. The fused semantic and sentiment features are jointly modeled through a dual-task structure for sentiment classification and sequence labeling. Experiments on 18,000 teaching-feedback samples show that the model achieves an accuracy of 87.2%, an F1 score of 84.1%, and an improvement-point completeness of 80.5%, outperforming static word embeddings, LSTM embeddings, and single-task baselines. The proposed method provides an engineering-oriented text-signal processing framework for semantic feature extraction, sentiment modeling, and intelligent feedback-analysis systems.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
R. A. M. de Kleijn, “Supporting student and teacher feedback literacy: an instructional model for student feedback processes,” Assessment & Evaluation in Higher Education, vol. 48, no. 2, pp. 186-200, 2023, doi: 10.1080/02602938.2021.1967283.
R. Zedan, “Student feedback as a predictor of learning motivation, academic achievement and classroom climate,” Education and self-development, vol. 16, no. 2, pp. 27-46, 2021, doi: 10.26907/esd.16.2.03.
D. Carless and N. Winstone, “Teacher feedback literacy and its interplay with student feedback literacy,” Teaching in higher education, vol. 28, no. 1, pp. 150-163, 2023, doi: 10.1080/13562517.2020.1782372.
A. A. Lipnevich and J. K. Smith, “Student-feedback interaction model: Revised,” Studies in Educational Evaluation, vol. 75, pp. 101208-101218, 2022, doi: 10.1016/j.stueduc.2022.101208.
J. H. Nieminen and D. Carless, “Feedback literacy: A critical review of an emerging concept,” Higher Education, vol. 85, no. 6, pp. 1381-1400, 2023, doi: 10.1007/s10734-022-00895-9.
D. Carless, “From teacher transmission of information to student feedback literacy: Activating the learner role in feedback processes,” Active Learning in Higher Education, vol. 23, no. 2, pp. 143-153, 2022, doi: 10.1177/1469787420945845.
M. A. Adarkwah, “The power of assessment feedback in teaching and learning: a narrative review and synthesis of the literature,” SN Social Sciences, vol. 1, no. 3, pp. 75-83, 2021, doi: 10.1007/s43545-021-00086-w.
Z. Gan, Z. An, and F. Liu, “Teacher feedback practices, student feedback motivation, and feedback behavior: how are they associated with learning outcomes? Frontiers in psychology,” 2021; 12:697045-697055, doi: 10.3389/fpsyg.2021.697045.
R. Ajjawi, F. Kent, J. Broadbent, et al., “Feedback that works: A realist review of feedback interventions for written tasks,” Studies in Higher Education, vol. 47, no. 7, pp. 1343-1356, 2022, doi: 10.1080/03075079.2021.1894115.
E. Lukwaro, K. Kalegele, and D. Nyambo, “A review on nlp techniques and associated challenges in extracting features from education data,” International Journal of Computing and Digital Systems, vol. 16, no. 1, pp. 962-972, 2024.
M. Liang, “Leveraging natural language processing for automated assessment and feedback production in virtual education settings,” Journal of Computational Methods in Sciences and Engineering, vol. 25, no. 3, pp. 2502-2515, 2025, doi: 10.1177/14727978251314556.
F. Qi, Y. Gao, M. Wang, T. Jiang, and Z. Li, “Data mining of online teaching evaluation based on deep learning,” Mathematics, vol. 12, no. 17, pp. 2692-2702, 2024, doi: 10.3390/math12172692.
R. Nawaz, Q. Sun, M. Shardlow, et al., “Leveraging AI and machine learning for national student survey: actionable insights from textual feedback to enhance quality of teaching and learning in UK’s higher education,” Applied Sciences, vol. 12, no. 1, pp. 514-524, 2022, doi: 10.3390/app12010514.
Sikandar Seemab, Muhammad Saqib Zaigham, Aisha Bhatti, and Hashim Khan and Uzma Arshad Mughal, “Sentiment analysis of student feedback in higher education using Natural Language Processing (NLP),” Contemporary Journal of Social Science Review, vol. 2, no. 04, pp. 1741-1750, 2024.
R. Harrison, L. Meyer, P. Rawstorne, et al., “Evaluating and enhancing quality in higher education teaching practice: A meta-review,” Studies in Higher Education, vol. 47, no. 1, pp. 80-96, 2022, doi: 10.1080/03075079.2020.1730315.
T. Shaik, X. Tao, Y. Li, et al., “A review of the trends and challenges in adopting natural language processing methods for education feedback analysis,” Ieee Access, vol. 10, pp. 56720-56739, 2022, doi: 10.1109/ACCESS.2022.3177752.
A. Botelho, S. Baral, J. A. Erickson, P. Benachamardi, and N. T. Heffernan, “Leveraging natural language processing to support automated assessment and feedback for student open responses in mathematics,” Journal of computer assisted learning, vol. 39, no. 3, pp. 823-840, 2023, doi: 10.1111/jcal.12793.
Z. Kastrati, F. Dalipi, A. S. Imran, et al., “Sentiment analysis of students’ feedback with NLP and deep learning: A systematic mapping study,” Applied Sciences, vol. 11, no. 9, pp. 3986-3996, 2021, doi: 10.3390/app11093986.
K. Sangeetha and D. Prabha, “Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction,” Computer Systems Science & Engineering, vol. 40, no. 2, pp. 735-745, 2022, doi: 10.32604/csse.2022.019310.
C. Dervenis, G. Kanakis, and P. Fitsilis, “Sentiment analysis of student feedback: A comparative study employing lexicon and machine learning techniques,” Studies in Educational Evaluation, vol. 83, pp. 101406-101416, 2024, doi: 10.1016/j.stueduc.2024.101406.
K. Okoye, A. Arrona-Palacios, C. Camacho-Zuñiga, et al., “Towards teaching analytics: a contextual model for analysis of students’ evaluation of teaching through text mining and machine learning classification,” Education and Information Technologies, vol. 27, no. 3, pp. 3891-3933, 2022, doi: 10.1007/s10639-021-10751-5.
Tracii Ryan, Michael Henderson, and Kris Ryan and Gregor Kennedy, “Feedback in higher education: Aligning academic intent and student sensemaking,” Teaching in Higher Education, vol. 29, no. 4, pp. 860-875, 2024, doi: 10.1080/13562517.2022.2029394.
M. Alzaid and F. Fkih, “Sentiment analysis of students’ feedback on e-learning using a hybrid fuzzy model,” Applied Sciences, vol. 13, no. 23, pp. 12956-12966, 2023, doi: 10.3390/app132312956.
C. Dann, P. Redmond, M. Fanshawe, et al., “Making sense of student feedback and engagement using artificial intelligence,” Australasian Journal of Educational Technology, vol. 40, no. 3, pp. 58-76, 2024, doi: 10.14742/ajet.8903.
C. R. Drue and C. A. Bifulco, “Beyond the numbers: How directors and chairs interpret student feedback to equitably evaluate teaching,” Journal of Academic Ethics, vol. 23, pp. 1499-1521, 2025, doi: 10.1007/s10805-025-09611-5.
Z. Liu, C. Wen, Z. Su, et al., “Emotion-semantic-aware dual contrastive learning for epistemic emotion identification of learner-generated reviews in MOOCs,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 11, pp. 16464-16477, 2023, doi: 10.1109/TNNLS.2023.3294636.
A. S. Sunar and M. S. Khalid, “Natural language processing of student’s feedback to instructors: A systematic review,” IEEE Transactions on Learning Technologies, vol. 17, pp. 741-753, 2023, doi: 10.1109/TLT.2023.3330531.
K. Masood, M. A. Khan, U. Saeed, et al., “Semantic analysis to identify students’ feedback,” The Computer Journal, vol. 65, no. 4, pp. 918-925, 2022, doi: 10.1093/comjnl/bxaa130.
P. Ren, L. Yang, and F. Luo, “Automatic scoring of student feedback for teaching evaluation based on aspect-level sentiment analysis,” Education and information technologies, vol. 28, no. 1, pp. 797-814, 2023, doi: 10.1007/s10639-022-11151-z.
D. Jatain, M. Niranjanamurthy, and P. Dayananda, “A hybrid bio-inspired fuzzy feature selection approach for opinion mining of learner comments,” SN Computer Science, vol. 5, no. 1, pp. 135-145, 2024, doi: 10.1007/s42979-023-02526-1.
X. Ma, “English Teaching in Artificial Intelligence-based Higher Vocational Education Using Machine Learning Techniques for Students’ Feedback Analysis and Course Selection Recommendation,” Journal of Universal Computer Science (JUCS), vol. 28, no. 9, pp. 898-908, 2022, doi: 10.3897/jucs.94160.
Asad Abdi, Gayane Sedrakyan, Bernard Veldkamp, and Jos van Hillegersberg and Stéphanie M van den Berg, “Students feedback analysis model using deep learning-based method and linguistic knowledge for intelligent educational systems,” Soft Computing, vol. 27, no. 19, pp. 14073-14094, 2023, doi: 10.1007/s00500-023-07926-2.
I. Zyout and M. Zyout, “Sentiment analysis of student feedback using attention-based RNN and transformer embedding,” Int J Artif Intell, vol. 13, no. 2, pp. 2173-2184, 2024, doi: 10.11591/ijai.v13.i2.pp2173-2184.
G. Suthakar A R and Sandesh B J.Enhanced image captioning with positional and dual attention using deep convolution long short-term memory and emotional feedback mechanism, “Multimedia Tools and Applications,” 2026; 85(3). doi: 10.1007/s11042-026-21394-4.
H. Shuqin and C. Raga R, “A deep learning model for student sentiment analysis on course reviews,” IEEE Access, vol. 12, pp. 136747-136758, 2024, doi: 10.1109/ACCESS.2024.3463793.