Skip to main content
SpringerLink
Log in

Explainable AI to understand study interest of engineering students

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Students are the future of a nation. Personalizing student interests in higher education courses is one of the biggest challenges in higher education. Various AI and ML approaches have been used to study student behaviour. Existing AI and ML algorithms are used to identify features for various fields, such as behavioural analysis, economic analysis, image processing, and personalized medicine. However, there are major concerns about the interpretability and understandability of the decision made by a model. This is because most AI algorithms are black-box models. In this study, explain- able AI (XAI) aims to break the black box nature of an algorithm. In this study, XAI is used to identify engineering students’ interests, and BRB and SP-LIME are used to explain which attributes are critical to their studies. We also used (PCA) for feature selection to identify the student cohort. Clustering the cohort helps to analyse the between influential features in terms of engineering discipline selection. The results show that there are some valuable factors that influence their study and, ultimately, the future of a nation.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

The datasets of our study were publicly available from our github repository (https://github.com/linkoncuetbd/Student-survey-data).

References

  • Aggarwal, M. (2021). Modeling a decision-maker’s choice behavior through perceived values. IEEE Transactions Systems, Man, and Cybernetics: Systems, 51(3), 1933–1944.

    Google Scholar 

  • Burkart, N., & Huber, M. F. (2021). A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research, 70, 245–317.

    Article  MathSciNet  Google Scholar 

  • Chen, Y.-J., Tanaka, K., Tanaka, M., Tsai, S.-H., & Wang, H. O. (2021). A novel path-following-method-based polynomial fuzzy control design. IEEE Transactions on Cybernetics, 51(6), 2993–3003.

    Article  PubMed  Google Scholar 

  • Choi, T.-M., Chan, H. K., & Yue, X. (2017). Recent development in big data analytics for business operations and risk management., 47(1), 81–92.

    Google Scholar 

  • Dutta, S., Behera, L., & Nahavandi, S. (2021). Skill learning from human demonstrations using dynamical regressive models for multitask applications. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(1), 659–672.

    Article  Google Scholar 

  • Fiok, K., Karwowski, W., Gutierrez, E., & Wilamowski, M. (2021). Analysis of sentiment in tweets addressed to a single domain-specific twitter account: comparison of model performance and explainability of predictions. Expert Systems with Applications, 115771.

  • Gautam, C., Tiwari, A., Suresh, S., & Ahuja, K. (2021). Adaptive online learning with regularized kernel for one-class classification. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(3), 1917–1932.

    Google Scholar 

  • Guo, F., Li, G., Wen, C., Wang, L., & Meng, Z. (2021). An accelerated distributed gradient-based algorithm for constrained optimization with application to economic dispatch in a large-scale power system. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4), 2041–2053.

    Article  Google Scholar 

  • Güven, C., Seipel, D., & Atzmueller, M. (2021). Applying asp for knowledge-based link prediction with explanation generation in feature-rich networks. IEEE Transactions on Network Science and Engineering, 8(2), 1305–1315.

    Article  Google Scholar 

  • Howell, S. K., Wicaksono, H., Yuce, B., McGlinn, K., & Rezgui, Y. (2019). User centered neuro-fuzzy energy management through semantic-based optimization., 49(9), 3278–3292.

    Google Scholar 

  • Islam, M. A., Anderson, D. T., Pinar, A. J., Havens, T. C., Scott, G., & Keller, J. M. (2020). Enabling explainable fusion in deep learning with fuzzy integral neural networks. IEEE Transactions on Fuzzy Systems, 28(7), 1291–1300.

    Article  Google Scholar 

  • Yang, J.-B., Liu, J., Wang, J., Sii, H.-S., & Wang, H.-W. (2006). Belief rule-base inference methodology using the evidential reasoning approach-RIMER. IEEE Trans. Syst., Man, Cybern., 36(2):266–285

  • Kamal, M. S., Chowdhury, L., Dey, N., Fong, S. J., & Santosh, K. (2021a). Explainable ai to analyze outcomes of spike neural network in covid-19 chest x-rays. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp 3408–3415

  • Kamal, M. S., Dey, N., Chowdhury, L., Hasan, S. I., & Santosh, K. (2022). Explainable AI for glaucoma prediction analysis to understand risk factors in treatment planning., 71, 1–9.

    Google Scholar 

  • Kamal, M. S., Northcote, A., Chowdhury, L., Dey, N., Crespo, R. G., & Herrera-Viedma, E. (2021). Alzheimer’s patient analysis using image and gene expression data and explainable-ai to present associated genes. IEEE Transactions on Instrumentation and Measurement, 70, 1–7.

    Article  Google Scholar 

  • Liu, C., Wang, K., Wang, Y., & Yuan, X. (2021a). Learning deep multi-manifold structure feature representation for quality prediction with an industrial application. 1–1

  • Liu, Z.-Z., Wang, Y., & Wang, B.-C. (2021). newblock Indicator-based constrained multiobjective evolutionary algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(9), 5414–5426.

    Article  Google Scholar 

  • Mansoori, A., & Effati, S. (2021). Parametric ncp-based recurrent neural network model: A new strategy to solve fuzzy nonconvex optimization problems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4), 2592–2601.

    Article  Google Scholar 

  • Solari, M., Vizquerra, M. I., & Engel, A. (2022). Students’ interests for personalized learning: an analysis guide.

  • Sun, Q., & Ge, Z. (2021). Deep learning for industrial KPI prediction: When ensemble learning meets semi-supervised data., 17(1), 260–269.

    Google Scholar 

  • Tian, Y., He, C., Cheng, R., & Zhang, X. (2021). A multistage evolutionary algorithm for better diversity preservation in multiobjective optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(9), 5880–5894.

    Article  Google Scholar 

  • Van Campenhout, R., Jerome, B., & Johnson, B. G. (2023). Engaging in student-centered educational data science through learning engineering. In A. Peña-Ayala (Ed.), Educational Data Science: Essentials, Approaches, and Tendencies (pp. 3–40). Springer Nature Singapore. Series Title: Big Data Management.

    Chapter  Google Scholar 

  • Wang, B.-C., Li, H.-X., Zhang, Q., & Wang, Y. (2021). Decomposition-based multiobjective optimization for constrained evolutionary optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(1), 574–587.

    Article  Google Scholar 

  • Wang, F.-Y., Filev, D. P., Pedrycz, W., Li, H., and White, C. C. (2018). Guest editorial from intelligent control to smart management of cyber-physical-social systems: A celebration of 70th anniversary of cybernetics by norbert wiener. 48(12), 3278–3279

  • Wang, Y., Sun, X., Li, J., & Yang, Y. (2021). Intelligent fault diagnosis with deep adversarial domain adaptation. IEEE Transactions on Instrumentation and Measurement, 70, 1–9.

    Article  Google Scholar 

  • Wu, X., Gu, Y., Tao, J., Li, G., Han, J., & Xiong, N. (2021). An effective data-driven cloud resource procurement scheme with personalized reserve prices. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(8), 4693–4705.

    Article  Google Scholar 

  • Yang, L., Song, S., Li, S., Chen, Y., & Huang, G. (2021). Graph embedding-based dimension reduction with extreme learning machine. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(7), 4262–4273.

    Article  Google Scholar 

  • Yang, Z., Yang, J., Rice, K., Hung, J.-L., & Du, X. (2020). Using convolutional neural network to recognize learning images for early warning of at-risk students., 13(3), 617–630.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering, JIS University, Kolkata, India

    Sourajit Ghosh & Biswarup Neogi

  2. Faculty of Engineering and Information Technology, The University of Technology Sydney, Sydney, Australia

    Md. Sarwar Kamal

  3. Computer Science and Engineering, East Delta University of Technology, Chattogram, Bangladesh

    Linkon Chowdhury

  4. Computer Science and Engineering, Techno International New Town, Kolkata, India

    Nilanjan Dey

  5. Department of Biomedical Engineering, University of Reading, Whiteknights, United Kingdom

    Robert Simon Sherratt

Authors
  1. Sourajit Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Md. Sarwar Kamal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Linkon Chowdhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Biswarup Neogi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nilanjan Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Robert Simon Sherratt
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed equally to the writing of this paper. All authors read and approved the final manuscript for submission.

Corresponding author

Correspondence to Md. Sarwar Kamal.

Ethics declarations

Competing interest

The authors declare that they have no competing interests.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghosh, S., Kamal, M.S., Chowdhury, L. et al. Explainable AI to understand study interest of engineering students. Educ Inf Technol 29, 4657–4672 (2024). https://doi.org/10.1007/s10639-023-11943-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-023-11943-x

Keywords

Search

Navigation