Explainable Artificial Intelligence in Education: A Comprehensive Review | SpringerLink
Skip to main content

Explainable Artificial Intelligence in Education: A Comprehensive Review

  • Conference paper
  • First Online:
Explainable Artificial Intelligence (xAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1902))

Included in the following conference series:

Abstract

Explainable artificial intelligence (AI) has drawn a lot of attention recently since AI systems are being employed more often across a variety of industries, including education. Building trust and increasing the efficacy of AI systems in educational settings requires the capacity to explain how they make decisions. This article provides a comprehensive review of the current level of explainable AI (XAI) research and its application to education. We begin with the challenges of XAI in education, the complexity of AI algorithms, and the necessity for transparency and interpretability. Furthermore, we discuss the obstacles involved with using AI in education, and explore several solutions, including human-AI collaboration, explainability techniques, and ethical and legal frameworks. Subsequently, we debate about the importance of developing new competencies and skills among students and educators to interact with AI effectively, as well as how XAI impacts politics and government. Finally, we provide recommendations for additional research in this field and suggest potential future directions for XAI in educational research and practice.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /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
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 14871
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 18589
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Abdi, S., Khosravi, H., Sadiq, S.: Modelling learners in crowdsourcing educational systems. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS, vol. 12164, pp. 3–9. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_1

    Chapter  Google Scholar 

  • Aditomo, A., Goodyear, P., Bliuc, A.-M., Ellis, R.A.: Inquiry-based learning in higher education: principal forms, educational objectives, and disciplinary variations. Stud. High. Educ. 38(9), 1239–1258 (2013)

    Article  Google Scholar 

  • Aditya, B.: Applied Machine Learning Explainability Techniques: Make ML Models Explainable and Trustworthy for Practical Applications Using LIME, SHAP, and More. Packt Publishing Ltd. (2022)

    Google Scholar 

  • Akour, I.A., Al-Maroof, R.S., Alfaisal, R., Salloum, S.A.: A conceptual framework for determining metaverse adoption in higher institutions of gulf area: an empirical study using hybrid SEM-ANN approach. Comput. Educ.: Artif. Intell. 3, 100052 (2022)

    Google Scholar 

  • Amer-Yahia, S.: Towards AI-powered data-driven education. Proc. VLDB Endow. 15(12), 3798–3806 (2022)

    Article  Google Scholar 

  • Amorim, E., Cançado, M., Veloso, A.: Automated essay scoring in the presence of biased ratings. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 229–237 (2018)

    Google Scholar 

  • Artificial intelligence—OECD. (n.d.). https://www.oecd.org/digital/artificial-intelligence/.Accessed 26 Apr 2023

  • Baker, R., Inventado, P.: Educational data mining and learning analytics, pp. 61–75 (2014). https://doi.org/10.1007/978-1-4614-3305-7_4

  • Baum, K., Mantel, S., Schmidt, E., Speith, T.: From responsibility to reason-giving explainable artificial intelligence. Philos. Technol. 35(1), 12 (2022). https://doi.org/10.1007/s13347-022-00510-w

    Article  Google Scholar 

  • Bhutoria, A.: Personalized education and artificial intelligence in the united states, china, and India: a systematic review using a human-in-the-loop model. Comput. Educ.: Artif. Intell. 3, 100068 (2022). https://doi.org/10.1016/j.caeai.2022.100068

    Article  Google Scholar 

  • Bischl, B., et al.: Openml benchmarking suites. ArXiv Preprint ArXiv:1708.03731 (2017)

  • Blikstein, P.: Gears of our childhood: constructionist toolkits, robotics, and physical computing, past and future. In: Proceedings of the 12th International Conference on Interaction Design and Children, pp. 173–182 (2013)

    Google Scholar 

  • Bojarski, M., et al.: End to end learning for self-driving cars. ArXiv Preprint ArXiv:1604.07316 (2016)

  • Bostrom, N., Yudkowsky, E.: The Ethics of artificial intelligence, pp. 57–69 (2018). https://doi.org/10.1201/9781351251389-4

  • Boyd-Graber, J., Satinoff, B., He, H., Daumé, I.: Besting the quiz master: crowdsourcing incremental classification games, p. 1301 (2012)

    Google Scholar 

  • Brusilovsky, P., Sosnovsky, S., Thaker, K.: The return of intelligent textbooks. AI Mag. 43(3), 337–340 (2022)

    Google Scholar 

  • Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Conference on Fairness, Accountability and Transparency, pp. 77–91 (2018)

    Google Scholar 

  • Cavanagh, T., Chen, B., Lahcen, R.A.M., Paradiso, J.R.: Constructing a design framework and pedagogical approach for adaptive learning in higher education: a practitioner’s perspective. Int. Rev. Res. Open Distrib. Learn. 21(1), 173–197 (2020)

    Google Scholar 

  • Chen, Z.: Artificial intelligence-virtual trainer: innovative didactics aimed at personalized training needs. J. Knowl. Econ. 1–19 (2022)

    Google Scholar 

  • Dabbagh, N., Kitsantas, A.: Personal learning environments, social media, and self-regulated learning: a natural formula for connecting formal and informal learning. Internet High. Educ. 15(1), 3–8 (2012). https://doi.org/10.1016/j.iheduc.2011.06.002

    Article  Google Scholar 

  • Dillenbourg, P., Jermann, P.: Technology for classroom orchestration. New Sci. Learn.: Cogn. Comput. Collab. Educ. 525–552 (2010)

    Google Scholar 

  • Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv:1702.08608 (2017). https://doi.org/10.48550/arXiv.1702.08608

  • Dwork, C., Feldman, V., Hardt, M., Pitassi, T., Reingold, O., Roth, A.: The reusable holdout: preserving validity in adaptive data analysis. Science 349(6248), 636–638 (2015)

    Article  MathSciNet  Google Scholar 

  • Ehsan, U., et al.: Human-centered explainable AI (HCXAI): beyond opening the black-box of AI. In: CHI Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022)

    Google Scholar 

  • Epstein, Z., Foppiani, N., Hilgard, S., Sharma, S., Glassman, E., Rand, D.: Do explanations increase the effectiveness of AI-crowd generated fake news warnings? In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 16, pp. 183–193 (2022)

    Google Scholar 

  • Green, E., Chia, R., Singh, D.: AI ethics and higher education—good practice and guidance for educators, learners, and institutions. Globethics.net (2022)

    Google Scholar 

  • Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)

    Article  Google Scholar 

  • Farrow, R.: The possibilities and limits of XAI in education: a socio-technical perspective. Learn. Media Technol. 1–14 (2023)

    Google Scholar 

  • Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80–89 (2018)

    Google Scholar 

  • Goodwin, N.L., Nilsson, S.R., Choong, J.J., Golden, S.A.: Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience. Curr. Opin. Neurobiol. 73, 102544 (2022)

    Article  Google Scholar 

  • Grand View Research. AI In Education Market Size & Share Report, 2022–2030, p. 100 (2021). https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-education-market-report

  • Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.-Z.: XAI—explainable artificial intelligence. Sci. Robot. 4(37), eaay7120 (2019)

    Google Scholar 

  • Herman, B.: The promise and peril of human evaluation for model interpretability. ArXiv Preprint ArXiv:1711.07414 (2017)

  • Holmes, W., Porayska-Pomsta, K.: The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates. Taylor & Francis (2022)

    Google Scholar 

  • HolonIQ. Artificial Intelligence in Education. 2023 Survey Insights (2023). https://www.holoniq.com/notes/artificial-intelligence-in-education-2023-survey-insights

  • Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudik, M., Wallach, H.: Improving fairness in machine learning systems: what do industry practitioners need? In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–16 (2019)

    Google Scholar 

  • Holzinger, A., Kieseberg, P., Weippl, E., Tjoa, A.M.: Current advances, trends and challenges of machine learning and knowledge extraction: from machine learning to explainable AI. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2018. LNCS, vol. 11015, pp. 1–8. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99740-7_1

    Chapter  Google Scholar 

  • Hwang, G.-J., Xie, H., Wah, B.W., Gašević, D.: Vision, challenges, roles and research issues of artificial intelligence in education. Comput. Educ.: Artif. Intell. 1, 100001 (2020). https://doi.org/10.1016/j.caeai.2020.100001

    Article  Google Scholar 

  • IBM Research. Project Debater (n.d.). https://research.ibm.com/interactive/project-debater/. Accessed 26 Apr 2023

  • Islam, M.R., Ahmed, M.U., Barua, S., Begum, S.: A Systematic review of explainable artificial intelligence in terms of different application domains and tasks. Appl. Sci. 12(3), Article 3 (2022). https://doi.org/10.3390/app12031353

  • Kasneci, E., et al.: ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Differ. 103, 102274 (2023)

    Article  Google Scholar 

  • Kelley, S., Ovchinnikov, A., Ramolete, G., Sureshbabu, K., Heinrich, A.: Tailoring explainable artificial intelligence: user preferences and profitability implications for firms. SSRN Scholarly Paper No. 4305480 (2022). https://doi.org/10.2139/ssrn.4305480

  • Khosravi, H., Gyamfi, G., Hanna, B.E., Lodge, J.: Fostering and supporting empirical research on evaluative judgement via a crowdsourced adaptive learning system. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, pp. 83–88 (2020). https://doi.org/10.1145/3375462.3375532

  • Khosravi, H., et al.: Explainable artificial intelligence in education. Comput. Educ.: Artif. Intell. 3, 100074 (2022). https://doi.org/10.1016/j.caeai.2022.100074

    Article  Google Scholar 

  • Kim, B., Park, J., Suh, J.: Transparency and accountability in AI decision support: explaining and visualizing convolutional neural networks for text information. Decis. Support Syst. 134, 113302 (2020)

    Article  Google Scholar 

  • Kim, J., Lee, H., Cho, Y.H.: Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Educ. Inf. Technol. 27(5), 6069–6104 (2022). https://doi.org/10.1007/s10639-021-10831-6

    Article  Google Scholar 

  • Kizilcec, R.F., Pérez-Sanagustín, M., Maldonado, J.J.: Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Comput. Educ. 104, 18–33 (2017)

    Article  Google Scholar 

  • Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., Mullainathan, S.: Human decisions and machine predictions. Q. J. Econ. 133(1), 237–293 (2018)

    Google Scholar 

  • Kolchenko, V.: Can modern AI replace teachers? Not so fast! Artificial intelligence and adaptive learning: personalized education in the AI age. HAPS Educator 22(3), 249–252 (2018)

    Article  Google Scholar 

  • Kumari, M., Chaudhary, A., Narayan, Y.: Explainable AI (XAI): a survey of current and future opportunities. In: Hassanien, A.E., Gupta, D., Singh, A.K., Garg, A. (eds.) Explainable Edge AI: A Futuristic Computing Perspective. Studies in Computational Intelligence, vol. 1072, pp. 53–71. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-18292-1_4

    Chapter  Google Scholar 

  • Laato, S., Tiainen, M., Najmul Islam, A.K.M., Mäntymäki, M.: How to explain AI systems to end users: a systematic literature review and research agenda. Internet Res. 32(7), 1–31 (2022). https://doi.org/10.1108/INTR-08-2021-0600

    Article  Google Scholar 

  • Laupichler, M., Aster, A., Tobias, R.: Delphi study for the development and preliminary validation of an item set for the assessment of non-experts’ AI literacy. Comput. Educ.: Artif. Intell. 4, 100126 (2023). https://doi.org/10.1016/j.caeai.2023.100126

    Article  Google Scholar 

  • Lipton, Z.C.: The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31–57 (2018)

    Article  Google Scholar 

  • Liu, B.: In AI we trust? Effects of agency locus and transparency on uncertainty reduction in human–AI interaction. J. Comput.-Mediat. Commun. 26(6), 384–402 (2021)

    Article  MathSciNet  Google Scholar 

  • Liyanagunawardena, T.R., Adams, A.A., Williams, S.A.: MOOCs: a systematic study of the published literature 2008–2012. Int. Rev. Res. Open Distrib. Learn. 14(3), 202–227 (2013)

    Google Scholar 

  • Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  • MacGillis, A.: The students left behind by remote learning. ProPublica (2020). https://www.propublica.org/article/the-students-left-behind-by-remote-learning

  • Manhiça, R., Santos, A., Cravino, J.: The impact of artificial intelligence on a learning management system in a higher education context: a position paper. In: Reis, A., Barroso, J., Martins, P., Jimoyiannis, A., Huang, R.Y.M., Henriques, R. (eds.) TECH-EDU 2022. CCIS, vol. 1720, pp. 454–460. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-22918-3_36

    Chapter  Google Scholar 

  • Meacham, M.: A brief history of AI and education. Int. J. Adult Non Formal Educ. 1–2 (2021)

    Google Scholar 

  • Minh, D., Wang, H.X., Li, Y.F., Nguyen, T.N.: Explainable artificial intelligence: a comprehensive review. Artif. Intell. Rev. 55(5), 3503–3568 (2022). https://doi.org/10.1007/s10462-021-10088-y

    Article  Google Scholar 

  • Mittelstadt, B.: Principles alone cannot guarantee ethical AI. Nat. Mach. Intell. 1(11), 501–507 (2019)

    Article  Google Scholar 

  • Moon, J., Rho, S., Baik, S.W.: Toward explainable electrical load forecasting of buildings: a comparative study of tree-based ensemble methods with Shapley values. Sustain. Energy Technol. Assess 54, 102888 (2022)

    Google Scholar 

  • Nagahisarchoghaei, M., et al.: An empirical survey on explainable ai technologies: recent trends, use-cases, and categories from technical and application perspectives. Electronics 12(5), Article 5 (2023). https://doi.org/10.3390/electronics12051092

  • Nandi, A., Pal, A.K.: Interpreting Machine Learning Models: Learn Model Interpretability and Explainability Methods. Springer, Heidelberg (2022)

    Google Scholar 

  • Needham, Mass.: Worldwide spending on AI-centric systems forecast to reach $154 billion in 2023, according to IDC. IDC: The Premier Global Market Intelligence Company (2023). https://www.idc.com/getdoc.jsp?containerId=prUS50454123

  • Nguyen, A., Ngo, H.N., Hong, Y., Dang, B., Nguyen, B.-P.T.: Ethical principles for artificial intelligence in education. Educ. Inf. Technol. 28(4), 4221–4241 (2023). https://doi.org/10.1007/s10639-022-11316-w

    Article  Google Scholar 

  • Ouyang, F., Zheng, L., Jiao, P.: Artificial intelligence in online higher education: a systematic review of empirical research from 2011 to 2020. Educ. Inf. Technol. 27(6), 7893–7925 (2022)

    Article  Google Scholar 

  • Raji, I.D., Scheuerman, M.K., Amironesei, R.: You can’t sit with us: exclusionary pedagogy in AI ethics education. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 515–525 (2021)

    Google Scholar 

  • Ratliff, K.: Building rapport and creating a sense of community: are relationships important in the online classroom? J. Online Learn. Res. Pract. 7(1) (2019)

    Google Scholar 

  • Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should i trust you?” Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)

    Google Scholar 

  • Saeed, W., Omlin, C.: Explainable AI (XAI): a systematic meta-survey of current challenges and future opportunities. Knowl.-Based Syst. 263, 110273 (2023). https://doi.org/10.1016/j.knosys.2023.110273

    Article  Google Scholar 

  • Salloum, S.A., Alshurideh, M., Elnagar, A., Shaalan, K.: Mining in educational data: review and future directions. In: Hassanien, A.-E., Azar, A.T., Gaber, T., Oliva, D., Tolba, F.M. (eds.) AICV 2020. AISC, vol. 1153, pp. 92–102. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44289-7_9

    Chapter  Google Scholar 

  • Samek, W., Müller, K.-R.: Towards explainable artificial intelligence. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Müller, K.-R. (eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700, pp. 5–22. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28954-6_1

    Chapter  Google Scholar 

  • Samek, W., Wiegand, T., Müller, K.-R.: Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. ArXiv Preprint ArXiv:1708.08296 (2017)

  • Sharma, H., Soetan, T., Farinloye, T., Mogaji, E., Noite, M.D.F.: AI adoption in universities in emerging economies: prospects, challenges and recommendations. In: Mogaji, E., Jain, V., Maringe, F., Hinson, R.E. (eds.) Re-imagining Educational Futures in Developing Countries: Lessons from Global Health Crises, pp. 159–174. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-88234-1_9

    Chapter  Google Scholar 

  • Shibani, A., Knight, S., Shum, S.B.: Educator perspectives on learning analytics in classroom practice. Internet High. Educ. 46, 100730 (2020)

    Article  Google Scholar 

  • Tadepalli, P., Fern, X., Dietterich, T.: Deep reading and learning. OREGON STATE UNIV CORVALLIS CORVALLIS, USA (2017)

    Google Scholar 

  • UNESCO. The promise of large-scale learning assessments: acknowledging limits to unlock opportunities. UNESCO (2019). https://unesdoc.unesco.org/ark:/48223/pf0000369697

  • Vapnik, V., Izmailov, R.: Rethinking statistical learning theory: learning using statistical invariants. Mach. Learn. 108(3), 381–423 (2019)

    Article  MathSciNet  Google Scholar 

  • Veale, M., Van Kleek, M., Binns, R.: Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2018)

    Google Scholar 

  • Vilone, G., Longo, L.: Explainable artificial intelligence: a systematic review. ArXiv Preprint ArXiv:2006.00093 (2020)

  • Walger, L., et al.: Artificial intelligence for the detection of focal cortical dysplasia: challenges in translating algorithms into clinical practice. Epilepsia (2023)

    Google Scholar 

  • Whalley, B., France, D., Park, J., Mauchline, A., Welsh, K.: Towards flexible personalized learning and the future educational system in the fourth industrial revolution in the wake of Covid-19. High. Educ. Pedag. 6(1), 79–99 (2021)

    Article  Google Scholar 

  • Woolf, B.P.: Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing e-Learning. Morgan Kaufmann (2010)

    Google Scholar 

  • Xia, X., Li, X.: Artificial intelligence for higher education development and teaching skills. Wirel. Commun. Mob. Comput. 2022 (2022)

    Google Scholar 

  • Xu, R., Baracaldo, N., Joshi, J.: Privacy-preserving machine learning: methods, challenges and directions. ArXiv Preprint ArXiv:2108.04417 (2021)

  • Yadav, A., et al.: A review of international models of computer science teacher education. In: Proceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education, pp. 65–93 (2022)

    Google Scholar 

  • Yakubu, M.N., Abubakar, A.M.: Applying machine learning approach to predict students’ performance in higher educational institutions. Kybernetes 51(2), 916–934 (2022)

    Article  Google Scholar 

  • Zawacki-Richter, O., Marín, V.I., Bond, M., Gouverneur, F.: Systematic review of research on artificial intelligence applications in higher education–where are the educators? Int. J. Educ. Technol. High. Educ. 16(1), 1–27 (2019)

    Article  Google Scholar 

  • Zeide, E.: Artificial intelligence in higher education: applications, promise and perils, and ethical questions. Educause Rev. 54(3) (2019)

    Google Scholar 

  • Zhai, X., et al.: A Review of artificial intelligence (AI) in education from 2010 to 2020. Complexity 2021, e8812542 (2021). https://doi.org/10.1155/2021/8812542

    Article  Google Scholar 

  • Zhang, J.: Computer assisted instruction system under artificial intelligence technology. Int. J. Emerg. Technol. Learn. (IJET) 16(5), 4–16 (2021)

    Article  Google Scholar 

  • Zheng, N., et al.: Hybrid-augmented intelligence: collaboration and cognition. Front. Inf. Technol. Electron. Eng. 18(2), 153–179 (2017). https://doi.org/10.1631/FITEE.1700053

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Blerta Abazi Chaushi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Chaushi, B.A., Selimi, B., Chaushi, A., Apostolova, M. (2023). Explainable Artificial Intelligence in Education: A Comprehensive Review. In: Longo, L. (eds) Explainable Artificial Intelligence. xAI 2023. Communications in Computer and Information Science, vol 1902. Springer, Cham. https://doi.org/10.1007/978-3-031-44067-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-44067-0_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44066-3

  • Online ISBN: 978-3-031-44067-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics