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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
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
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)
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
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)
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
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
Juhanak, L., Zounek, J., RohlĂkov, 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
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
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
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
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
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
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)
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
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
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
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)
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
Simo, H.: Big data: opportunities and privacy challenges, pp. 1–21 (2018)
Kaushik, M., Jain, A.: Challenges to big data security and privacy. Int. J. Comput. Sci. Inf. Technol. 5(3), 3042–3043 (2014)
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
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)
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)
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)
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
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
Niknam, S., Dhillon, H.S., Reed, J.H.: Federated learning for wireless communications: motivation, opportunities and challenges, pp. 1–6 (2019). arXiv:1908.06847v3
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
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
Merceron, A.: Educational data mining/learning analytics: methods, tasks and current trends. In: 2015 Proceedings of DeLFI Workshops, pp. 101–109 (2015)
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
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)
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
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-69143-1_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69142-4
Online ISBN: 978-3-030-69143-1
eBook Packages: Computer ScienceComputer Science (R0)