Skip to main content
Springer Nature Link
Log in

A Sentiment Analysis Based Approach for Exploring Student Feedback

  • Conference paper
  • First Online:
Innovative Technologies and Learning (ICITL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13449))

Included in the following conference series:

  • 1776 Accesses

Abstract

Student feedback is commonly used as a reliable source of information to evaluate learning outcomes and teaching quality. This feedback has proven to provide faculty not only with valuable insights into how students are learning, but also with an ideal opportunity to reflect on teaching resources and instructional strategies. However, given the increasing massive amounts of feedback that is available online, collecting and analyzing this data manually is not usually an easy task. The aim of this work is, therefore, to put forward a sentiment analysis classifier that is capable of categorizing student feedback as being either positive or negative. To this end, students’ reviews posted about online courses were automatically extracted, preprocessed and then fed into various machine learning algorithms. The findings of this analysis revealed that the Support Vector Machines (SVM) algorithm achieves the highest accuracy score (93.35%) and, thus, outperforms other implemented models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Blair, E., Valdez Noel, K.: Improving higher education practice through student evaluation systems: is the student voice being heard? Assess. Eval. High. Educ. 39(7), 879–894 (2014)

    Article  Google Scholar 

  2. Johnson, S.D., Aragon, S.R., Shaik, N.: Comparative analysis of learner satisfaction and learning outcomes in online and face-to-face learning environments. J. Interact. Learn. Res. 11(1), 29–49 (2000)

    Google Scholar 

  3. Faizi, R., El Fkihi, S., Ezzahid, S.S., El Afia, A.: Using sentiment analysis to derive business value. In: Proceedings of the 32nd International Business Information Management Association (IBIMA), pp. 15–16, November 2018, Seville, Spain (2018). ISBN: 978-0-9998551-1-9

    Google Scholar 

  4. Faizi, R., El Fkihi, S., El Afia, A.: Leveraging big data to improve customer experience. In: Proceedings of the 30th International Business Information Management Association Conference, IBIMA 2017 - Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth (2017)

    Google Scholar 

  5. Faizi, R., El Fkihi, S., El Afia, A.: Exploring the potentials of big data analytics in marketing. In: Proceedings of the 31st International Business Information Management Association Conference, IBIMA 2018: Innovation Management and Education Excellence through Vision 2020 (2018)

    Google Scholar 

  6. Colace, F., Casaburi, L., De Santo, M., Greco, L.: Sentiment detection in social networks and in collaborative learning environments. Comput. Hum. Behav. 51, 1061–1067 (2015)

    Article  Google Scholar 

  7. Barrón Estrada, M.L., Zatarain Cabada, R., Oramas Bustillos, R., Graff, M.: Opinion mining and emotion recognition applied to learning environments. Expert Syst. Appl. 150, 113265 (2020)

    Article  Google Scholar 

  8. Zhou, J., Ye, J.M.: Sentiment analysis in education research: a review of journal publications. Interact. Learn. Environ. 1–13 (2020)

    Google Scholar 

  9. El Fkihi, S., Ezzahid, S.S., Faizi, R., Chiheb, R.: Formative assessment in the era of big data analytics. In: Proceedings of the 32nd International Business Information Management Association (IBIMA), pp. 15–16 November 2018, Seville, Spain (2018). ISBN: 978-0-9998551-1-9

    Google Scholar 

  10. Menaha, R., Dhanaranjani, R., Rajalakshmi, T., Yogarubini, R.: Student feedback mining system using sentiment analysis. IJCATR 6, 1–69 (2017)

    Article  Google Scholar 

  11. Eng, T.H., Ibrahim, A.F., Shamsuddin, N.E.: Students’ perception: student feedback online (SuFO) in higher education. Procedia-Soc. Behav. Sci. 167, 109–116 (2015)

    Article  Google Scholar 

  12. Sangeetha, K., Prabha, D.: Sentiment analysis of student feedback using multi-head attention fusion model of word and context embedding for LSTM. J. Ambient. Intell. Humaniz. Comput. 12(3), 4117–4126 (2020). https://doi.org/10.1007/s12652-020-01791-9

    Article  Google Scholar 

  13. Singh, L.K., Devi, R.R.: Student feedback sentiment analysis: a review. Mater. Today Proc. (2021)

    Google Scholar 

  14. Faizi, R.: A sentiment-based approach to predict learners’ perceptions towards YouTube educational videos. In: Abraham, A., et al. (eds.) Innovations in Bio-Inspired Computing and Applications: Proceedings of the 12th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2021) Held During December 16–18, 2021, pp. 549–556. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96299-9_52

    Chapter  Google Scholar 

  15. Giang, N.T.P., Dien, T.T., Khoa, T.T.M.: Sentiment analysis for university students’ feedback. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) FICC 2020. AISC, vol. 1130, pp. 55–66. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39442-4_5

    Chapter  Google Scholar 

  16. Altrabsheh, N., Cocea, M., Fallahkhair, S.: Learning sentiment from students’ feedback for real-time interventions in classrooms. In: Bouchachia, A. (ed.) adaptive and intelligent systems, pp. 40–49. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11298-5_5

    Chapter  Google Scholar 

  17. Rakhmanov, O.: A comparative study on vectorization and classification techniques in sentiment analysis to classify student-lecturer comments. Procedia Comput. Sci. 178, 194–204 (2020)

    Article  Google Scholar 

  18. Kandhro, I.A., Chhajro, M.A., Kumar, K., Lashari, H.N., Khan, U.: Student feedback sentiment analysis model using various machine learning schemes: a review. Indian J. Sci. Technol. 12(14), 1–9 (2019)

    Google Scholar 

  19. Nasim, Z., Rajput, Q., Haider, S.: Sentiment analysis of student feedback using machine learning and lexicon based approaches. In 2017 International Conference on Research and Innovation in Information Systems (ICRIIS), pp. 1–6. IEEE (2017)

    Google Scholar 

  20. Dsouza, D.D., Deepika, D.P.N., Machado, E.J., Adesh, N.D.: Sentimental analysis of student feedback using machine learning techniques. Int. J. Recent Technol. Eng. 8(14), 986–991 (2019)

    Google Scholar 

  21. Faizi, R.: Moroccan higher education students’ and teachers’ perceptions towards using Web 2.0 technologies in language learning and teaching. Knowl. Manag. E-Learn. Int. J. (KM&EL) 10(1), 86–96 (2018)

    MathSciNet  Google Scholar 

  22. Snelson, C.: The benefits and challenges of YouTube as an educational resource. In: Hobbs, R. (ed.) The Routledge Companion to Media Education, Copyright, and Fair Use, pp. 203–218. Routledge (2018)

    Chapter  Google Scholar 

  23. Vieira, I., Lopes, A.P., Soares, F.: The potential benefits of using videos in higher education. In: Proceedings of EDULEARN14 Conference, pp. 0750–0756. IATED Publications (2014)

    Google Scholar 

  24. Faizi, R.: Teachers’ perceptions towards using Web 2.0 in language learning and teaching. Educ. Inf. Technol. 23(3), 1219–1230 (2017)

    Article  Google Scholar 

  25. Faizi, R., Rudneva, M.: Higher education students’ perceptions towards using Facebook as a learning platform. In: Huang, Y.-M., Lai, C.-F., Rocha, T. (eds.) ICITL 2021. LNCS, vol. 13117, pp. 548–554. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91540-7_56

    Chapter  Google Scholar 

  26. Faizi, R., El Fkihi, S.: Investigating the role of social networks in enhancing students’ learning experience: Facebook as a case study. Int. Assoc. Dev. Inf. Soc. (2018)

    Google Scholar 

  27. Faizi, R., El Fkihi, S.: Incorporating Web 2.0 technologies in education: opportunities and challenges. In: Proceedings of the 28th IBIMA conference on Vision 2020: Innovation Management, Development Sustainability, and Competitive Economic Growth, pp. 3242–3248 (2016)

    Google Scholar 

  28. Lewis, D.D.: Naive (Bayes) at forty: the independence assumption in information retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 4–15. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026666

    Chapter  Google Scholar 

  29. Tolles, J., Meurer, W.J.: Logistic regression: relating patient characteristics to outcomes. JAMA 316(5), 533–534 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ENSIAS, Mohammed V University in Rabat, Rabat, Morocco

    Rdouan Faizi & Sanaa El Fkihi

Authors
  1. Rdouan Faizi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanaa El Fkihi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rdouan Faizi .

Editor information

Editors and Affiliations

  1. National Cheng Kung University, Tainan City, Taiwan

    Yueh-Min Huang

  2. Southern Taiwan University of Science and Technology, Tainan City, Taiwan

    Shu-Chen Cheng

  3. University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    João Barroso

  4. OsloMet – Oslo Metropolitan University, Oslo, Norway

    Frode Eika Sandnes

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Faizi, R., El Fkihi, S. (2022). A Sentiment Analysis Based Approach for Exploring Student Feedback. In: Huang, YM., Cheng, SC., Barroso, J., Sandnes, F.E. (eds) Innovative Technologies and Learning. ICITL 2022. Lecture Notes in Computer Science, vol 13449. Springer, Cham. https://doi.org/10.1007/978-3-031-15273-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-15273-3_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15272-6

  • Online ISBN: 978-3-031-15273-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation