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Privacy Preservation in Mobile-Based Learning Systems: Current Trends, Methodologies, Challenges, Opportunities and Future Direction

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Information and Communication Technology and Applications (ICTA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1350))

Abstract

The adoption of mobile technologies in education are evolving like in the business and health sectors. The design of user-centric platform to enable individuals participate in the activities of learning and teaching is currently area of research. The Learning Management Systems (LMS) area assists learners and academic activities but, it continues to fall short of desired impact due to huge demands of the application. More importantly, the mobile applications offer enormous convenience not without the possibility of eavesdropping and maliciously exploiting data about users. The original structure of mobile learning requires that data and processing heads have centralized entity, which is not possible in wireless application arrangements due to communication overhead of transmitting raw data to central learning processor. This led to the use of distributed mobile learning structure, which preserve privacy of learners. This study discusses the challenges, current trends, methodology, opportunities and future direction of privacy preservation in mobile-based learning systems. The study highlighted the use of learners’ private data and behavioral activities by LMS especially in understanding the needs of learners as well as improvement of their experiences. But, it raises concerns about the risks of learners’ privacy on LMS due the mining processes of learners, which were not considered in existing related studies in literature.

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References

  1. Singh, H., Miah, S.J.: Design of a mobile-based learning management system for incorporating employment demands: case context of an Australian University. Educ. Inf. Technol. 24(2), 995–1014 (2018). https://doi.org/10.1007/s10639-018-9816-1

    Article  Google Scholar 

  2. Wang, Y., Zheng, N., Xu, M., Qiao, T., Zhang, Q., Yan, F.: Hierarchical identifier: application to user privacy eavesdropping on mobile payment app. Sensors 19(14), 1–9 (2019). https://doi.org/10.3390/s19143052

    Article  Google Scholar 

  3. Ketthari, M.T., Rajendran, S.: Privacy preserving data mining using hiding maximum utility item first algorithm by means of grey wolf optimisation algorithm. Int. J. Bus. Intell. Data Min. 14(3), 401–418 (2019)

    Google Scholar 

  4. Mohanrao, M., Karthik, S.: Privacy preserving for global data using ensemble approach. In: International Conference on Computer Vision and Machine Learning, vol. 1228, pp. 1–7 (2019). https://doi.org/10.1088/1742-6596/1228/1/012046

  5. Nagaraj, K., Sharvani, G.S., Sridhar, A.: Encrypting and preserving sensitive attributes in customer churn data using novel dragonfly based pseudonymizer approach. Information 10(9), 1–21 (2019)

    Article  Google Scholar 

  6. Normadhi, N.B.A., Shuib, L., Nasir, H.N., Bimba, A., Idris, N., Balakrishnan, V.: Identification of personal traits in adaptive learning environment: systematic literature review. Comput. Educ. 130, 168–190 (2019). https://doi.org/10.1016/j.compedu.2018.11.005

    Article  Google Scholar 

  7. Aldiab, A., Chowdhury, H., Kootsookos, A., Alam, F., Allhibi, H.: Utilization of learning management systems (LMSs) in higher utilization of learning management systems in higher education system: a case review for Saudi Arabia. Energy Procedia 160, 731–737 (2019). https://doi.org/10.1016/j.egypro.2019.02.186

    Article  Google Scholar 

  8. Juhanak, L., Zounek, J., Rohlíkov, L.: Using process mining to analyze students’ quiz-taking behavior patterns in a learning management system. Comput. Hum. Behav. J. 92, 496–506 (2017). https://doi.org/10.1016/j.chb.2017.12.015

    Article  Google Scholar 

  9. Sarker, N.I., Wu, M., Cao, Q., Alam, G.M.M., Li, D.: Leveraging digital technology for better learning and education: a systematic literature review. Int. J. Inf. Educ. Technol. 9(7), 453–461 (2019). https://doi.org/10.18178/ijiet.2019.9.7.1246

    Article  Google Scholar 

  10. Ahmed, Y.A., Ahmad, M.N., Ahmad, N., Zakaria, N.H.: Social media for knowledge-sharing: a systematic literature review. Telematics Inform. 37, 72–112 (2018). https://doi.org/10.1016/j.tele.2018.01.015

    Article  Google Scholar 

  11. Cantabella, M., et al.: Analysis of student behavior in learning management systems through a big data framework. Future Gener. Comput. Syst. 90, 262–272 (2019). https://doi.org/10.1016/j.future.2018.08.003

    Article  Google Scholar 

  12. Ismail, A., Shehab, A., El-Henawy, I.M.: Healthcare analysis in smart big data analytics: reviews, challenges and recommendations. In: Hassanien, A.E., Elhoseny, M., Ahmed, S.H., Singh, A.K. (eds.) Security in Smart Cities: Models, Applications, and Challenges. LNITI, pp. 27–45. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01560-2_2

    Chapter  Google Scholar 

  13. Alharthi, A.D., Spichkova, M., Hamilton, M.: Sustainability requirements for eLearning systems: a systematic literature review and analysis. Requirements Eng. 24(4), 523–543 (2018). https://doi.org/10.1007/s00766-018-0299-9

    Article  Google Scholar 

  14. Antonius, H., Widjaja, E., Santoso, S.W., Petrus, S., Cahyadi, J.: The enhancement of learning management system in teaching learning process with the UTAUT2 and trust model. In: 2019 International Conference on Information Management and Technology, vol. 1, pp. 309–313. IEEE (2019)

    Google Scholar 

  15. Garone, A., et al.: Clustering university teaching staff through UTAUT: implications for the acceptance of a new learning management system. Br. J. Educ. Technol. 50(5), 2466–2483 (2019). https://doi.org/10.1111/bjet.12867

    Article  Google Scholar 

  16. Kaur, A., Kaur, K.: Systematic literature review of mobile application development and testing effort estimation. J. King Saud Univ.-Comput. Inf. Sci. (2018). https://doi.org/10.1016/j.jksuci.2018.11.002

  17. Karle, T., Vora, D.: PRIVACY preservation in big data using anonymization techniques. In: 2017 International Conference on Data Management, Analytics and Innovation, pp. 340–343 (2017). https://doi.org/https://doi.org/10.1109/ICDMAI.2017.8073538

  18. Bashari, B., Akbarzadeh, N., Ataei, P., Khakbiz, Y.: Security and privacy challenges in big data era. Int. J. Control Theory Appl. 9(43), 437–448 (2016)

    Google Scholar 

  19. Fatt, Q.K., Ramadas, A.: The usefulness and challenges of big data in healthcare. J. Healthc. Commun. 3(2), 1–4 (2018). https://doi.org/10.4172/2472-1654.100131

    Article  Google Scholar 

  20. Simo, H.: Big data: opportunities and privacy challenges, pp. 1–21 (2018)

    Google Scholar 

  21. Kaushik, M., Jain, A.: Challenges to big data security and privacy. Int. J. Comput. Sci. Inf. Technol. 5(3), 3042–3043 (2014)

    Google Scholar 

  22. Baqer, M., Azad, A.K., Vasilakos, A.: Security and privacy challenges in mobile cloud computing: survey and way ahead. J. Netw. Comput. Appl. 84, 38–54 (2017). https://doi.org/10.1016/j.jnca.2017.02.001

    Article  Google Scholar 

  23. Avella, J.T., Kebritchi, M., Nunn, S.G., Kanai, T.: Learning analytics methods, benefits, and challenges in higher education: a systematic literature review. Online Learn. 20(2), 13–29 (2016)

    Google Scholar 

  24. Kambourakis, G.: Security and privacy in m-learning and beyond: challenges and state-of-the-art. Int. J. U- and E-Serv. Sci. Technol. 6(3), 67–84 (2013)

    Google Scholar 

  25. Gursoy, M.E., Inan, A., Nergiz, M.E., Saygin, Y.: Privacy-preserving learning analytics: challenges and techniques. IEEE Trans. Learn. Technol. 114, 1–4 (2018)

    Google Scholar 

  26. Manogaran, G., Thota, C., Lopez, D.: HCI Challenges and Privacy Preservation in Big Data Security. The Advances in Human and Social Aspects of Technology (AHSAT) Book Series, pp. 1–23 (2018). https://doi.org/https://doi.org/10.4018/978-1-5225-2863-0.ch001

  27. Kabassi, K., Alepis, E.: Learning analytics in distance and mobile learning for designing personalised software. In: Virvou, M., Alepis, E., Tsihrintzis, G.A., Jain, L.C. (eds.) Machine Learning Paradigms. ISRL, vol. 158, pp. 185–203. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-13743-4_10

    Chapter  Google Scholar 

  28. Niknam, S., Dhillon, H.S., Reed, J.H.: Federated learning for wireless communications: motivation, opportunities and challenges, pp. 1–6 (2019). arXiv:1908.06847v3

  29. Esmaeilzadeh, P.: The effects of public concern for information privacy on the adoption of health information exchanges (HIEs) by healthcare entities. Health Commun. 34, 1202–1211 (2018). https://doi.org/10.1080/10410236.2018.1471336

    Article  Google Scholar 

  30. Torra, V., Navarro-Arribas, G.: Big data privacy and anonymization. In: Lehmann, A., Whitehouse, D., Fischer-Hübner, S., Fritsch, L., Raab, C. (eds.) Privacy and Identity Management, vol. 498, pp. 15–26. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-55783-0_2

    Chapter  Google Scholar 

  31. Merceron, A.: Educational data mining/learning analytics: methods, tasks and current trends. In: 2015 Proceedings of DeLFI Workshops, pp. 101–109 (2015)

    Google Scholar 

  32. Wang, Y., Tian, Z., Zhang, H., Su, S., Shi, W.: A privacy preserving scheme for nearest neighbor query. Sensor 18(8), 1–4 (2018). https://doi.org/10.3390/s18082440

    Article  Google Scholar 

  33. Hadioui, A., Faddouli, N.E., Touimi, Y.B., Bennani, S.: Machine learning based on big data extraction of massive educational knowledge. IJET 12(11), 151–167 (2017)

    Google Scholar 

  34. Plamondon, R., Pirlo, G., Anquetil, É., Rémi, C., Teulings, H.-L., Nakagawa, M.: Personal digital bodyguards for e-security, e-learning and e-health: a prospective survey. Pattern Recogn. 81, 633–659 (2018). https://doi.org/10.1016/j.patcog.2018.04.012

    Article  Google Scholar 

  35. Omolade, A.O.: Predictors of use of mobile applications by university students in Oyo State, Nigeria. J. Inf. Sci. Syst. Technol. 1(1), 34–48 (2017)

    Google Scholar 

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Authors and Affiliations

  1. Federal University of Technology, Minna, Nigeria

    Muhammad Kudu Muhammad, Ishaq Oyebisi Oyefolahan, Olayemi Mikail Olaniyi & Ojeniyi Joseph Adebayo

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  1. Muhammad Kudu Muhammad
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  2. Ishaq Oyebisi Oyefolahan
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  3. Olayemi Mikail Olaniyi
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  4. Ojeniyi Joseph Adebayo
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Correspondence to Muhammad Kudu Muhammad .

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Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. Federal University of Technology Minna, Minna, Nigeria

    Bilkisu Muhammad-Bello

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Muhammad, M.K., Oyefolahan, I.O., Olaniyi, O.M., Adebayo, O.J. (2021). Privacy Preservation in Mobile-Based Learning Systems: Current Trends, Methodologies, Challenges, Opportunities and Future Direction. In: Misra, S., Muhammad-Bello, B. (eds) Information and Communication Technology and Applications. ICTA 2020. Communications in Computer and Information Science, vol 1350. Springer, Cham. https://doi.org/10.1007/978-3-030-69143-1_40

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  • DOI: https://doi.org/10.1007/978-3-030-69143-1_40

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