Abstract
This paper introduces a comprehensive mining pipeline designed to analyze course feedback comments efficiently. The proposed methodology begins with generating embeddings for student comments, applying Association Rule Mining (ARM) to identify key features, subsequently applying clustering to ascertain optimal k, culminating in the most interpretable topics using Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) techniques. Our findings reveal that NMF outperforms LDA in terms of topic interpretability and coherence, achieving a notable coherence score of 0.42. This performance enhancement is corroborated through expert manual evaluation, confirming that NMF, combined with precise feature selection and optimal topic quantity, provides a more effective framework for analyzing course feedback.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
openAI. 2023: ChatGPT (mar 14 version). https://chat.openai.com/chat
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Chaturvedi, R., Ezeife, C.: Clustering examples in web-based tutoring systems based on relevance of concepts. In: 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), pp. 1–6. IEEE (2020)
Gencoglu, B., Helms-Lorenz, M., Maulana, R., Jansen, E.P., Gencoglu, O.: Machine and expert judgments of student perceptions of teaching behavior in secondary education: added value of topic modeling with big data. Comput. Educ. 193, 104682 (2023)
Göbel, K., Wyss, C., Neuber, K., Raaflaub, M.: Student feedback as a source for reflection in practical phases of teacher education. Student feedback on teaching in schools: Using student perceptions for the development of teaching and teachers, pp. 173–189 (2021)
Grönberg, N., Knutas, A., Hynninen, T., Hujala, M.: Palaute: an online text mining tool for analyzing written student course feedback. IEEE Access 9, 134518–134529 (2021)
He, J.: Big data set from ratemyprofessor.com for professors’ teaching evaluation. https://doi.org/10.17632/fvtfjyvw7d.2, https://data.mendeley.com/datasets/fvtfjyvw7d/2
Hujala, M., Knutas, A., Hynninen, T., Arminen, H.: Improving the quality of teaching by utilising written student feedback: a streamlined process. Comput. Educ. 157, 103965 (2020)
Koufakou, A., Gosselin, J., Guo, D.: Using data mining to extract knowledge from student evaluation comments in undergraduate courses. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 3138–3142. IEEE (2016)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Onan, A.: Mining opinions from instructor evaluation reviews: a deep learning approach. Comput. Appl. Eng. Educ. 28(1), 117–138 (2020)
Shastry, P., Prakash, C.: Comparative analysis of LDA, LSA and NMF topic modelling for web data. In: AIP Conference Proceedings. vol. 2901. AIP Publishing (2023)
Steyn, C., Davies, C., Sambo, A.: Eliciting student feedback for course development: the application of a qualitative course evaluation tool among business research students. Assess. Eval. High. Educ. 44(1), 11–24 (2019)
Vangara, R., et al.: Finding the number of latent topics with semantic non-negative matrix factorization. IEEE Access 9, 117217–117231 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Prateek, P., Pais, N.A., Chaturvedi, R. (2025). Integrative Mining Pipeline for Improved Reflections of Course Feedback. In: Delir Haghighi, P., Greguš, M., Kotsis, G., Khalil, I. (eds) Information Integration and Web Intelligence. iiWAS 2024. Lecture Notes in Computer Science, vol 15343. Springer, Cham. https://doi.org/10.1007/978-3-031-78093-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-78093-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-78092-9
Online ISBN: 978-3-031-78093-6
eBook Packages: Computer ScienceComputer Science (R0)